ArticlePDF Available

Abstract and Figures

Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.
Content may be subject to copyright.
Review Article Building Thermal, Lighting,
and Acoustics Modeling
Building simulation: Ten challenges
Tianzhen Hong (), Jared Langevin, Kaiyu Sun
Building Technology and Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
Buildings consume more than one-third of the world’s primary energy. Reducing energy use and
greenhouse-gas emissions in the buildings sector through energy conservation and efficiency
improvements constitutes a key strategy for achieving global energy and environmental goals.
Building performance simulation has been increasingly used as a tool for designing, operating
and retrofitting buildings to save energy and utility costs. However, opportunities remain for
researchers, software developers, practitioners and policymakers to maximize the value of building
performance simulation in the design and operation of low energy buildings and communities
that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a
large scale. This paper presents ten challenges that highlight some of the most important issues in
building performance simulation, covering the full building life cycle and a wide range of
modeling scales. The formulation and discussion of each challenge aims to provide insights into
the state-of-the-art and future research opportunities for each topic, and to inspire new questions
from young researchers in this field.
building energy use,
energy efficiency,
building performance simulation,
energy modeling,
building life cycle,
zero-net-energy buildings
Article History
Received: 3 February 2018
Revised: 22 March 2018
Accepted: 29 March 2018
© Tsinghua University Press and
Springer-Verlag GmbH Germany,
part of Springer Nature 2018
The buildings sector consumes about 40% of primary energy
in the United States and European countries and about
25%–30% in developing countries like China. In the United
States, federal, state and local governments set stringent
energy goals for new and existing buildings. For example,
in the 2016 multi-year program plan, the U.S. Department
of Energy’s Building Technologies Office (2016) set a goal
to reduce the energy use intensity (EUI) of buildings by
30% by 2030 and 50% over the long-term. At the state
level, California’s long-term energy efficiency strategic plan
(California Public Utilities Commission 2008) stipulates that
all new residential buildings must be zero-net-energy (ZNE)
by 2020, all new commercial buildings must be ZNE by 2030,
and 50% of existing commercial buildings must be retrofitted
to ZNE by 2030. At the city level, the City of San Francisco
requires all new municipal construction projects of 5,000
square feet or larger to be LEED Gold certified; several other
U.S. cities have similar new construction requirements.
Building performance simulation (BPS)—also known as
building simulation, building energy modeling, or energy
simulation—has played a growing role in the design and
operation of low energy, high-performance buildings and
development of policies that drive the achievement of the
aforementioned energy goals. BPS is defined as the use of
computational mathematical models to represent the physical
characteristics, expected or actual operation, and control
strategies of a building (or buildings) and its (their) energy
systems. BPS calculations include building energy flows,
airflows, energy use, thermal comfort and other indoor
environmental quality indexes (e.g., glare).1
BPS has a decades-long history of development, beginning
with the replacement of manual procedures with computing
tools to determine HVAC loads in the 1960s (Clark 2001 and
2015; Hensen and Lamberts 2011; BEMBook 2018). Several
review articles (e.g., Hong et al. 2000; Li and Wen 2014;
Clarke 2015; Clarke and Hensen 2015; Wang and Zhai 2016;
Østergård et al. 2016; Harish and Kumar 2016) survey key
developments, applications and challenges for the BPS
1 For a more detailed overview of BPS, see the U.S. Department of Energy’s
“101” articles (Roth 2017) on building energy modeling and major use cases
including architectural design, HVAC design and operation, building
performance rating, and building stock analysis.
Hong et al. / Building Simulation
field across its history.
BPS development has been particularly pronounced
in the past ten years, as demonstrated by the founding of
two new journals in 2008, Journal of Building Performance
Simulation and Building Simulation, as well as the growth
of the International Building Performance Simulation
Association (IBPSA), which was formed in 1987. In parallel,
several international research efforts under the International
Energy Agency (IEA)’s Energy in Buildings and Communities
(EBC) Programme have advanced the application of BPS to
support the design of buildings and communities (Hong
2018). Key BPS-related IEA EBC projects include: Annex 1,
which developed algorithms to determine load and energy
of existing buidings; Annex 10, which focused on building
HVAC system simulation; Annex 21 and 43, which developed
standard methods and test cases to validate and benchmark
BPS programs; Annex 30, which provided best practices to
integrate simulation in various phases of building design;
Annex 53, which used BPS to analyze impact of six
influencing factors of real building performance (Yoshino
et al. 2017); Annex 58, which studied methods and collected
data for full-scale empirical validation of detailed BPS
Programs; Annex 60, which developed a Modelica-based
library of building energy system component models (Wetter
et al. 2015a); Annex 66, which developed new data, methods,
modeling tools and case studies to understand, model and
quantify occupant behavior in buildings (Yan et al. 2017);
and Annex 22, 51 and 73, which studied energy efficient
From a practical perspective, BPS is commonly used to:
(1) perform load calculations in support of HVAC equipment
selection and sizing, (2) demonstrate the code compliance
of a building by comparing the energy performance of the
proposed design with the code baseline, and (3) evaluate
and compare design scenarios. For further details, the book
Building Performance Simulation for Design and Operation
(Hensen and Lamberts 2011) provides a comprehensive
overview of how building performance simulation is used in
the complete building life-cycle from conception to demolition.
Note that although the use of BPS in the building design
process is widespread, its use in the operation, control and
retrofit of existing buildings remains limited.
Figure 1 outlines a theoretical BPS ecosystem in which
successful applications integrate users (i.e., energy modelers),
programs, data and resource support. These three com-
ponents are discussed further below.
Regarding BPS users, the Rocky Mountain Institute has
developed the concept of “black belt energy modeling”
(Rocky Mountain Institute 2010) to set forth BPS user
expectations, training materials, and professional development
paths. Under this concept, becoming a master user requires
a depth and breadth of knowledge about engineering, building
science and BPS programs. Additionally, ASHRAE’s
Fundamentals Handbook Chapter 19, Energy Estimating
and Modeling Methods, covers fundamental concepts for
energy modelers, particularly those new to the field. ASHRAE’s
building energy modeling professional certification further
ensures that BPS users have the training needed to develop
and perform successful energy simulations.
Regarding BPS programs, though many are available,2
no program is perfect in terms of accuracy and ease-of-use
(Zhu et al. 2013; Zhou et al. 2014). Moreover, available BPS
programs are used to answer a wide range of questions
for architects, engineers and other stakeholders, and it is
important to select a BPS program that is appropriate for
the particular application of interest—indeed, this notion is
the basis of the fit-for-purpose modeling concept (Gaetani
et al. 2016). Stretching a BPS program beyond its intended
scope of use should be avoided; this practice may lead to
modeling errors and at minimum requires a deep unders-
tanding of the BPS program in question.
Finally, regarding data and resource support, inadequate
efforts to collect supporting data for BPS underpin the
“Garbage In, Garbage Out” aphorism. When modeling new
buildings, for example, users must anticipate how the building
will be used and accurately specify design performance goals.
When modeling existing buildings, on-site inspections and
energy audits can be used to establish reliable input data
for energy models. Sound data collection is not replaced
by parallel efforts, e.g., model calibration that attempts to
fine-tune key model input parameters. As data for many
parameters are needed to build detailed energy models
using BPS programs such as EnergyPlus (U.S. Department
of Energy 2018a), user experience is needed to focus
data collection around the most important model input
Looking ahead, several studies have surveyed state-of-
the-art in BPS research and highlighted key challenges and
research items for future BPS development. For example,
Hong et al. (2000) presented seven use categories of BPS
and predicted continued BPS development in five areas:
(1) integrating BPS with knowledge-based systems to
support decision making, (2) using BPS in early design stage,
(3) integrating information monitoring and diagnostic systems
(Piette et al. 2001) with BPS for building energy management
and control, (4) integrating multiple BPS programs in the
building life cycle, and (5) using virtual reality technology
to enable digital building design and operation experience.
Despite some advances in these five areas over the last 20
years, each remains a significant challenge.
In another study, Clarke (2015) developed 16 propositions
2 The building energy software tools directory,, lists hundreds of BPS programs
at various levels of fidelity, accuracy, complexity and ease of use.
Hong et al. / Building Simulation
for IBPSA to advance the BPS field, emphasizing the need
for high-integrity emulations of building performance
through BPS while acknowledging the need for accordant
changes to company work practices, user-interfaces, support,
and accreditation, tool screening, scientific communication,
and process management. Clarke and Hensen (2015) further
summarized the state-of-the-art in building performance
simulation, outlining issues relating to a high integrity
representation of building performance, identifying emerg-
ing challenges that will dictate new directions for BPS
development, and characterizing barriers to collaborative
development in the field.
Wang and Zhai (2016) provided an overview of BPS
advancements and trends for development and application
achieved between 1987 and 2014, focusing on six different
topics including ventilation performance prediction, whole
building energy and thermal load simulation, lighting and
daylighting modeling, building information modeling, indoor
acoustic simulation, and life cycle analysis of buildings.
Building on these forward-looking studies, this paper
aims to pinpoint and discuss the ten most important
challenges currently facing the BPS research area, discussing
potential solutions to each challenge while acknowledging
its technical complexity and significance to a variety of
stakeholders. Table 1 lists the selected challenges and outlines
the practical significance of addressing each. As seen in the
table, the challenges cover several existing or emerging areas
of BPS research and application, including: (1) understanding
the gap between expected and actual building performance to
achieve targeted design performance goals, (2) understanding
and quantifying human-building interactions, (3) modeling
existing buildings and their large energy use contributions to
the building sector, (4) supporting the design and operation of
ZNE and grid-responsive buildings, (5) large-scale building
technology adoption, evaluation, and modeling to inform
energy policy making in city, state and federal governments,
and (6) integrating BPS across the building life cycle.
The challenges were selected based on the preceding
literature review and reflect recent advances in the
technologies and software capabilities that support BPS, as
well as broader advances in the BPS field and building
simulation community as a whole. These challenges also
Fig. 1 A theoretical building performance simulation ecosystem integrates users, programs, data and resource support
Table 1 Ten challenges of building performance simulation
Challenge Significance
Addressing the building performance gap BPS supports verification of building performance goals and ratings/certifications
Modeling human-building interactions BPS incorporates models of environmentally adaptive occupant behavior, which
has significant impacts on building energy performance
Energy model calibration
Modeling operational faults in buildings
Modeling building operations, controls, and retrofits
BPS supports operational improvements and energy efficiency improvements/retrofits
in existing buildings
Zero-net-energy (ZNE) and grid-responsive buildings BPS supports the design of ZNE buildings and representation of building energy
load dynamics needed to deploy building efficiency as a grid resource
Urban-scale building energy modeling BPS supports city-scale building energy efficiency measures needed to achieve
energy/environmental goals
Evaluating the energy-saving potential of building technologies
at national or regional scales
Modeling energy efficient technology adoption
BPS supports government decision making on building efficiency research, technology
development and assessment
Integrated modeling and simulation BPS supports decision making across the entire building life cycle
Hong et al. / Building Simulation
reflect the wide range of potential BPS applications, spanning
multiple stages in the building life cycle from design to
operation and retrofit, and multiple scales of analysis from
individual buildings and building occupants to cities, utility
regions, and the national building stock (Fig. 2). The paper’s
broad coverage of potential BPS tasks aims to highlight
common research needs surrounding data collection,
standardization, and integration; model development and
selection; and the development of modeling workflows that
are of practical use.
We provide an overview of each challenge, discuss why
it is important, highlight recent advances in addressing it,
and propose potential future research directions. Note that
herein, the scope of the term building “performance” is
limited to energy performance simulation, though the authors
acknowledge that important challenges exist in modeling
other types of performance metrics such as thermal comfort,
indoor air quality and CFD in the built environment.
Fig. 2 The ten BPS challenges described in this paper span multiple
stages in the building life cycle and scales of focus, which range
from the individual building level to the national building stock
Ten challenges
1. Addressing the building performance gap
With the increasing demand for more energy efficient
buildings, the buildings industry is faced with the challenge
of ensuring that the energy performance predicted during
the design stage is achieved once a building is in use. However,
previous studies have identified a significant “performance
gap” between designed and actual energy performance of
both commercial and residential buildings (Frei et al. 2017;
Calì et al. 2016; Van Dronkelaar et al. 2016; Robinson et al.
2016; de Wilde 2014), also known as the “credibility gap”
(Bordass et al. 2004; Dasgupta et al. 2012). The performance
gap between designed and measured energy use is best
illustrated by Fig. 3, from a study by the New Buildings
Institute (Turner and Frankel 2008). Here, it is clear that
while measured and design energy use intensities (EUI) are
correlated, they often differ substantially from one another
in absolute terms.
Fig. 3 Measured versus design EUIs (Turner and Frankel 2008)
Energy performance gaps may originate in all stages of
the building development process, from design to construction
to operation. For example, factors such as miscommunication
between designers, engineers, and contractors, and inadequate
quality control during construction may contribute to
observed performance gaps.
Three types of performance gap are identified by de
Wilde (2014) from the energy calculations perspective:
(1) a mismatch between “first principle” energy models
and measurements undertaken on actual buildings, (2) a
mismatch between data-driven empirical approaches and
measurements from real buildings, (3) a mismatch between
the energy ratings provided by compliance test methods
and energy display certificates as enshrined in regulation.
The performance gap is not only limited to energy and has
recently been extended to other metrics such as embodied
emissions (Pomponi and Moncaster 2018). In this paper,
however, the focus is placed on the energy performance gap.
When faced with an energy performance gap, building
owners may suggest the designers mis-specified their energy
model, while designers might argue that the building is
used in unexpected ways and/or improperly operated and
managed (Bordass et al. 2004). The performance gap erodes
the credibility of the design and engineering sectors of the
building industry; in turn, this leads to skepticism about
high-performance building concepts, undermining public
confidence in the role of building energy efficiency in
national carbon reduction efforts (de Wilde 2014). Indeed,
bridging the energy performance gap is essential if designers
and engineers are to influence the delivery of high-
performance buildings that meet ambitious targets such as
zero-net-energy (ZNE, covered in a subsequent section).
Bridging this gap will also improve the ability of buildings
to adapt to changing use conditions by “occupant proofing”
or “climate change proofing” (de Wilde 2014).
Multiple factors are potentially responsible for the energy
performance gap. According to IEA Annex 53’s findings,
building energy consumption is mainly influenced by
six factors: (1) climate (Cui et al. 2017; Hong et al. 2013),
Hong et al. / Building Simulation
(2) building envelope (Fang et al. 2014), (3) building services
and energy systems, (4) building operation and maintenance
(Lin and Hong 2013), (5) occupant activities and behavior
(D’Oca et al. 2018) and (6) indoor environmental quality
provided. The latter three factors, related to human behavior,
can have an influence as great as or greater than the
former three factors, which are building-related (Yoshino
et al. 2017).
Indeed, Li et al. (2014) studied 51 high-performance
office buildings in the US, Europe, China and other parts of
Asia, and discovered that climate, building size, or technology
do not determine energy use alone; occupant behavior,
building operation and maintenance also significantly
influence realized energy savings. In particular, occupant
behavior has been identified as a major factor contributing
to the discrepancy between simulation predictions and real
energy use (Yan et al. 2017; Ahn et al. 2017). User-related
factors are stochastic and have been found to vary
substantially from design values in buildings; accordingly,
new scientific approaches are needed to describe and quantify
the influence of occupant behavior and account for this
influence in the building simulation process (see next section).
Moreover, the existence of “rebound” and “prebound”
effects can further lead to the over- or under- estimation of
the effects of occupant behaviors on energy use (Haas and
Biermayr 2000; Sorrell et al. 2009; Hens et al. 2010; Galvin
2014; Sunikka-Blank and Galvin 2012).
In recent years, several research efforts have focused on
reducing the energy performance gap. Eschewing traditional,
deterministic energy simulation, for example, Sun (2014)
proposed a probabilistic framework of predicting energy
consumption using computation-based uncertainty quan-
tification, which shows improvement in model prediction
capabilities and reduces prediction errors for case study
buildings. IEA Annex 66 proposed scientific approaches
to reduce the energy performance gap by representing
occupant behaviors in a standardized quantitative way,
going further by integrating simulated behaviors with current
BPS programs (Yan et al. 2017). Regarding behavior modeling
approaches, Markov Chain (Wang et al. 2011), probabilistic
(Sun et al. 2014), and random walk (Ahn et al. 2017) models
of occupancy have been proposed.
Post-occupancy evaluation (POE) has also proven
essential in understanding the energy performance gap
and can potentially be used to inform better predictions,
improving the input assumptions used in detailed energy
modeling and closing the building performance feedback
loop (Van Dronkelaar et al. 2016; Menezes et al. 2012;
Choi et al. 2012). POE has been embedded into Building
Information Modeling (BIM) to engage different stakeholders
in the collaborative effort of continuous building performance
improvement (Göçer et al. 2015).
Efforts to bridge the performance gap span the building
design stage, construction and operational stage (de Wilde
2014; Jones et al. 2015; Burman et al. 2014; Dasgupta et al.
2012). Regarding the design stage, design guidance and
reports have been developed to raise awareness amongst
clients and design teams, ensuring that design intent and
responsibilities are communicated and leaving no room
for error during building construction. Regarding the
construction stage, efforts such as Building with Care
attempt to increase the quality of the construction delivery
process (Tofield 2012). Finally, regarding the operational
stage, standardized data collection and monitoring techniques
have been used to reduce uncertainty in collected operational
data. The handover between the construction and operation
stages is also being improved by new programs such as “Soft
Landings” (BSRIA and UBT 2014), which was developed in
the UK to keep designers and constructors involved in
verifying the performance of buildings beyond completion.
In order to continue bridging the energy performance
gap caused by miscommunication and misalignment of
different roles within a building development process, future
work should focus on developing integrated methods of
building design, construction, operation, and commissioning.
Such methods will enable a more thorough and accurate
exchange of information across the building life cycle. As
stated in the Zero Carbon Hub (2014) report, this effort will
involve fundamental changes to the traditional building
industry. At the same time, more work is needed to address
the issue of occupant behavior, among the strongest influences
on the performance gap. Improved representation of occupant
behavior in detailed energy modeling requires a better
understanding of the nature of occupants’ interactions with
different types of buildings, including how occupants
use energy and respond to socio/technical energy saving
initiatives. This topic is the subject of the next section.
2. Modeling the human-building interaction for occupant-
centric building design and operation
Building occupants interact with indoor environments and
control systems through their presence in a space and the
adaptive actions they take to maintain personal environmental
satisfaction. These human-building interactions (HBIs)
affect both energy use and occupant comfort outcomes
and are therefore central to building design and operation
(D’Oca et al. 2018). Occupants’ behavioral interactions with
buildings are of a wide variety, including the passive exchange
of heat with space; opening and closing doors and windows;
adjustment of thermostat settings, light settings, blinds
and shades, or clothing levels; use of personal heating and
cooling devices; and consumption of warm or cold drinks.
Each interaction may be motivated by a number of factors
Hong et al. / Building Simulation
ranging from the physical environment and availability of
control options to occupants’ personal preferences and
environmental attitudes, social interactions, and broader
cultural context (Fabi et al. 2012; Gunay et al. 2013; Langevin
et al. 2015a).
Modeling capabilities that accurately simulate HBI are
needed for both design-stage tools for BPS and controls
system software that enables more efficient operation and
management of building energy services. In the building
design stage, the ability to represent expected occupant
behaviors and their effects on simulated energy flows can
support design strategies that are robust to these behaviors.
In the building operation stage, models of building occupants,
their comfort and behavior can support model-predictive
control (MPC) and human-in-the-loop (HIL) control schemes
that minimize building energy use while maximizing occupant
Efforts to model the human-building interaction face
the scientific challenge of accurately representing behavioral
diversity and its potential determinants as well as the
practical challenge of implementing such models in widely
used building design and operation software. Observed
behaviors tend to vary widely across and within occupant
populations and can even vary within an individual upon
repeated observation (Yan et al. 2015). Moreover, the set of
variables that best explains the observed variation in behaviors
depends strongly on the particular building context of
interest, characteristics of the occupant population, and the
type of behavior(s) being studied. Top-down, equation-based
modeling frameworks (Haldi and Robinson 2009, 2010),
which are the most tractable to implement as part of BPS
program, cannot explicitly represent the causal structures
that yield behavioral diversity across individuals and
populations. Agent-based models (ABMs) (Azar and Menassa
2012; Langevin et al. 2015b; Lee and Malkawi 2014; Chandra
Putra et al. 2017), which can simulate individual-level
decision-making processes for multiple behaviors at once
and social interactions, offer greater flexibility in exploring
causality; however, these models require more resources to
develop and implement in BPS programs (e.g., more data,
modeler time, computing power).
Efforts to put data behind HBI models bring their own
challenges (Wagner et al. 2018; Sun and Hong 2017b). Certain
occupant behaviors may be difficult or impossible to measure
without the use of self-report surveys, which introduce
potential recall bias and limit the frequency and duration of
measurement campaigns. Moreover, given the wide array
of potential occupant behaviors and the heterogeneity in
occupant characteristics and building contexts, in situ
occupant data collection efforts must target a large sample
of occupants to yield broadly representative insights about
observed behaviors. In practice, resources may not allow
such large-scale sampling of occupants in real building
settings. Additionally, cross-sectional (point-in-time) field
studies may fail to capture a full range of behavior outcomes
or detect statistically significant relationships between these
outcomes and other measured variables (e.g., environmental
conditions). While longitudinal field studies are better suited
to capturing time-resolved variation in behaviors, these
studies are expensive to implement and are often constrained
to smaller sample sizes. Laboratory experiments offer the
most control over occupant sample selection and exposure
to environmental conditions, but may omit important aspects
of a field setting (e.g., availability of natural light, social
HBI models and datasets that overcome these challenges
will improve the accuracy of building energy modeling and
support occupant-centric building control schemes with large
energy savings potential. Previous research has established
occupant behavior as one of six influencing variables on
real building energy use (Yoshino et al. 2017) and a key
source of uncertainty in predicting energy use; multiple
studies report the sensitivity of simulated energy use
outcomes to changes in occupant behavior parameters (up
to 150%) (Clevenger and Haymaker 2006; Hong and Lin
2013). Behavior-related energy model inputs also strongly
affect simulated indoor environmental conditions and thermal
comfort performance (Langevin et al. 2016). Regarding
building controls, HBI models can serve as proxies for direct
occupant measurements in control schemes that require
occupant feedback, such as occupant-based MPC (Mirakhorli
and Dong 2016) and indirect or hybrid human-in-the-loop
controls (Munir et al. 2013). Importantly, modeled HBI
proxies reduce occupant reporting and measurement burdens,
a key barrier to the long-term implementation of such
control schemes. Previous studies report the potential for
these schemes to yield from 10%–40% HVAC and lighting
energy savings while maintaining or improving comfort
(Ghahramani et al. 2014; Nagy et al. 2015; Williams et al.
Recent progress in HBI modeling and data collection
has been strongly supported by the IEA EBC Annex 66:
Definition and Simulation of Occupant Behavior, which has
driven rapid growth in the number of studies concerning
building occupant behavior (Yan et al. 2017). Progress
can be grouped into three categories: fundamental model
development, data collection methods, and model integration
with building design and operation software. Model
development in the Annex has found that equation-based
discrete-time or discrete-event Markov and survival models
can accurately describe the adjustment of lights, blinds,
windows, and the use of plug-in equipment. Agent-based
models, though not a focus of the Annex, have continued
to grow in use throughout the occupant behavior modeling
Hong et al. / Building Simulation
community. Most ABMs have been used in an exploratory
fashion without validation efforts, e.g., Papadopoulos and
Azar (2016); Chandra Putra et al. (2017); attempts at ABM
validation have shown promising predictive capabilities,
but for limited occupant samples (Langevin et al. 2015b).
HBI data collection advances supported through the
Annex are categorized as in situ, laboratory and surveys. In
situ experiments have benefited from advances in occupant
sensing technologies, which include continuous logging
of occupant presence and movement, control state (e.g.,
window position, thermostat setting, light level), and plug
loads. Sensors that transmit occupant data wirelessly are
now widely available, reducing maintenance burden for
longer-term experiments. Nevertheless, up front sensor costs
remain high (U.S. Department of Energy 2015b). Online
surveys have been used as lower cost substitutes for sensor
measurements that can also directly explore the social and
psychological determinants of behavior (D’Oca et al. 2017).
Emerging approaches include mixed method data collection
(Creswell 2006), which uses both qualitative and quantitative
measurement techniques, and immersive virtual environments
(IVEs) (Heydarian and Becerik-Gerber 2017), which blend
aspects of laboratory and field studies.
State-of-the-art integration of HBI models with widely
used building energy simulation programs leverages the
Functional Mockup Interface (FMI) standard (Otter et al.
2011) for co-simulation of behavior and energy use. An
occupant behavior Functional Mockup Unit (FMU) (obFMU,
Hong et al. 2016c) was developed to support the exchange
of behavior data in a standardized XML format (obXML,
Hong et al. 2015b,c) with building energy simulation
programs like EnergyPlus and ESP-r. Here, energy models
simulate environmental conditions to use as inputs to the
behavior model, while the behavior model provides the
energy model with updated control states. Additionally,
some studies have attempted to integrate occupant models
with building controls systems. For example, the use of
predictive occupancy models in MPC schemes has been
explored (Mirakhorli and Dong 2016), and Bayesian schemes
for learning personalized environmental preference profiles
that tune HVAC operation have also been developed (Lee
et al. 2017).
Going forward, multiple areas of HBI research develo-
pment are needed. First, meta-analyses of existing HBI
modeling studies should quantitatively compare existing
equation- and non-equation-based models using a consistent
set of metrics. Metrics might include measures of model
accuracy, parsimony, and uncertainty across the range of
behaviors typically studied in the residential and commercial
building sectors. Ideally, model comparison and validation
would be performed by those outside the research team
that developed each model, with data that were not used to
develop the models (Yan et al. 2017).
Data for meta-analyses should be compiled from the
large number of existing field and laboratory HBI studies, a
second area of focus for future work. Indeed, while the
number of HBI measurement studies has grown dramatically
in recent years, a single, easily accessible repository for HBI
data does not yet exist. Such repositories have spurred
research advancements in related fields—see, for example,
the ASHRAE RP-884 Database (de Dear 1998), which
supported the development of an adaptive thermal comfort
standard and continues to drive progress in thermal comfort
Third, future work should seek further dissemination
of HBI modeling capabilities in widely used BPS programs.
While methods for co-simulation of behavior and energy
models have been successfully demonstrated by tools like
obFMU, dynamic HBI modeling capabilities are not yet
offered with new builds of major energy simulation engines.
Additional work is needed to add HBI modules to standard
releases of these engines and associated interfaces like
OpenStudio, DesignBuilder, and Sefaira. This work can
continue to build on the FMI standard, which allows HBI
models to be executed by any software that adheres to the
same. Given the large numbers of behavior models that may
be selected, HBI menus should encourage a fit-for-purpose
approach to model selection (Gaetani et al. 2016).
Finally, future work must re-examine the typical building-
or zone-level scale of HBI model application in the face of
growing interest in occupant-centric building operations
(U.S. Department of Energy 2018b) and in interactions
between buildings and the utility grid (Nemtzow 2017).
At the occupant scale, advances in both human-in-the-
loop control approaches and technologies for localized
environmental conditioning (U.S. Department of Energy
Advanced Research Projects Agency 2014) demand the
representation of individual preferences, actions, and their
impacts across a zone or building-level occupant population.
At the grid scale, accurate models of hourly energy load
shapes (Electric Power Research Institute 2018) that consider
HBI are needed to design efficiency programs that shift
these loads away from peak periods of energy use while
maintaining occupant comfort. Agent-based models, which
can generate aggregate-level HBI predictions for building-
to-grid operations from individual-level representations
of occupant and operator decisions, warrant further
consideration for these new scales of model application.
3. Model calibration
As the first challenge section described, previous studies
have indicated significant discrepancies between simulated
energy use from building energy models and actual measured
Hong et al. / Building Simulation
data (Balaras et al. 2016; Yoshino et al. 2017; Yin et al. 2014;
Karlsson et al. 2007; La Fleur et al. 2017; Maile et al. 2012).
Again, this undermines confidence in model predictions
and curtails adoption of building energy performance tools
during design, commissioning, operation (Samuelson et al.
2016; Coakley et al. 2014) and retrofit (Johnson 2017; Heo
et al. 2012). To address this issue, building energy models
must be improved to closely represent the actual performance
of modeled buildings. This can be achieved through model
calibration, the process of using an existing BPS program
and “tuning” or calibrating various inputs to the program
so that observed energy use matches closely with that
predicted by the simulation program (Reddy 2006).
Calibration can significantly improve the validity of and
confidence in energy models while being used to: (1) compare
the cost-effectiveness of ECMs in the design stage, (2) assess
various performance optimization measures during the
operational stage (Coakley et al. 2014), (3) implement
continuous commissioning or fault detection measures to
identify equipment malfunction (Reddy 2006; Zibin et al.
2016), and (4) support decision making in existing building
retrofits, assessing the benefits and uncertainties associated
with each (Reddy 2006; Heo et al. 2012).
While the simulation accuracy of building energy models
is determined by thousands of parameters, there are usually
limited measured data available as calibration inputs. This
makes calibration a highly under-determined and over-
parameterized problem without a unique solution. Moreover,
the collection of detailed sub-metered data may entail
considerable time and cost. Currently, building energy
simulation models are considered “calibrated” if they meet
the internationally-accepted criteria defined by ASHRAE
Guideline 14 (ANSI/ASHRAE 2014). However, there are
numerous models that meet these criteria and may be
considered “calibrated” for the same building; non-unique
solutions therefore remain a key issue with model calibration.
In addition, it should be noted that current calibration
criteria relate solely to predicted energy consumption and
do not account for input parameter uncertainty or inaccuracy,
the accuracy of BPS program, or the accuracy of the simulated
environment (e.g., temperature profiles) (Coakley et al.
Manual and automated are the two main categories of
model calibration. As manual calibration is a user-driven
process of trial and error, it not only requires professional
engineering expertise and experience, but is also very time-
consuming and cost-ineffective. Therefore, researchers have
expended significant effort to improve existing manual
calibration procedures to make them more systematic and
efficient. In parallel, a number of innovative automated
calibration methods have been developed. Recent progress
in this area includes:
Researchers are breaking down the traditional whole
building calibration problem into sub-pieces that are easier
to solve. For example, Cacabelos et al. (2017) divided the
entire building into different sub-models and calibrates
them separately according to output temperatures, delivery
energy or power consumption, varying the most influential
parameters during different periods of the year. The results
show that the new multi-stage procedure can achieve better
accuracy than those obtained with a global calibration.
Mihai and Zmeureanu (2017) proposed a bottom-up
calibration technique based on Building Automation
System trend data, which starts with zone level calibration
with supply airflow rate to each zone, indoor air temperature
and cooling load, followed by AHU level calibration. The
results show that the AHU model was calibrated naturally
on top of most calibrated zones, which avoids any
additional tuning through the trial-and-error method.
Occupancy patterns and internal loads contribute
significantly to the discrepancy between the predicted
and actual energy consumption and have thus formed a
key focus for input calibration (Sun et al. 2014; Hong
et al. 2017a; Liang et al. 2016; Yan et al. 2017; Sun and
Hong 2017a). Kim et al. (2017) applied the occupancy
and plug-load schedules derived from metered electric
use data to building energy model calibration, substantially
improving the accuracy of building energy modeling
results. Similarly, Lam et al. (2014) adopted occupant
behavior data mining techniques to generate occupancy
schedules, using them in the model calibration process
and achieving better calibration accuracy. Sun et al.
(2014) proposed a stochastic model to describe overtime
occupancy, and used the model to generate overtime
occupancy schedules, which were applied to energy model
calibration and improved model accuracy.
Advanced optimization techniques are being used to
improve the performance of optimization-based automated
calibration methods. For instance, Hong et al. (2017b)
developed an automatic calibration model using the genetic
algorithm (GA) with the optimization objective of
approaching the minimum CV(RMSE). The CV(RMSE)
was reduced from 18.10% to 12.62%. Multiple other studies
have used the GA algorithm for auto-calibration (Ramos
Ruiz et al. 2016; Andrade-Cabrera et al. 2016, 2017).
The Autotune project developed by Oak Ridge National
Laboratory leverages supercomputing, large simulation
ensembles, and big data mining with multiple machine
learning algorithms to allow auto-calibration of energy
simulations (Garrett and New 2015).
Several studies address the computational cost of auto-
calibration, which has slowed the adoption of such
techniques. Specifically, the computational cost can be
reduced by fitting a statistical emulator or meta-model,
Hong et al. / Building Simulation
to replace the physical model. A typical application
example is Bayesian calibration, where meta-models are
often adopted in combination with Bayesian calibration
approaches (Kristensen et al. 2017; Kim and Park 2016).
Manfren et al. (2013) successfully integrated model-driven
and data-driven procedures by training a Gaussian process
meta-model with computer simulation data and using it
in a Bayesian calibration process, reducing computational
cost without sacrificing much accuracy. Lim and Zhai
(2017a) evaluated the performance of five types of meta-
models and their effects on the Bayesian calibration
based on computing time and calibration accuracy. It
was found that all five meta-models significantly reduced
the computing time compared with the original process
without meta-models; a Gaussian process emulator was
found to be the most accurate but most time-consuming
approach, while a multiple linear regression model was
the fastest approach but showed the worst performance.
Li et al. (2016b) addressed the issue of high computing time
for a standard Gaussian process emulator by introducing
a lightweight approach with the linear regression emulator.
The regression emulator calibrates more quickly while
maintaining similar performance compared to the standard
Gaussian process emulator. Lastly, for optimization-based
auto-calibration approaches, feature selection and sampling
is a very important step; emerging methods include Latin
Hypercube Sampling (Kim and Park 2016; Yun and Song
2017), Markov chain Monte Carlo (MCMC) (Garrett and
New 2015) and No-U-Turn-Sampler MCMC (Chong
et al. 2017), etc.
Optimization is based on mathematical methods and
typically lacks critical inputs from physics and engineering
perspectives, thus sometimes leading to unreasonable
calibrated results. Sun et al. (2016) addressed this issue by
combining the strengths of both manual and automated
calibration based on pattern recognition, encompassing
more engineering insights and experience than purely
mathematical optimization-based methods for auto-
An automatic assisted calibration tool was developed
that couples building automation system trend data with
building commissioning tasks. This tool reduces the
considerable time required to manually process and analyze
large sets of trend data for use in calibrated simulation
(Zibin et al. 2016).
In general, while calibration techniques have improved
greatly in recent years, current calibration criteria from
ASHRAE guideline 14 may not be sufficient for com-
prehensively assessing the accuracy of calibration results.
Such criteria specify broad ranges of allowable error in the
total predicted energy consumption of a building but do
not specifically address uncertainty or inaccuracy in input
parameters or zone-level environmental data. More com-
prehensive criteria are needed to accommodate different
levels and purposes of model calibration.
Looking ahead, one important area for advances in BPS
calibration is Urban Building Energy Modeling (UBEM),
which is increasingly used to explore energy efficiency
solutions at the urban or district scales (see subsequent
section on this topic). As there are at least hundreds
of buildings involved in UBEM, it is extremely time-
consuming to collect detailed information and calibrate the
buildings one-by-one to guarantee the accuracy. Future
work must, therefore, shift attention from single building
calibration to urban-scale calibration, supporting the growing
interest in urban-scale modeling.
The most common approach for formulating a UBEM
involves segmenting a building stock into archetypes,
characterizing each type, and validating the model by
comparing its output to aggregated measured energy con-
sumption. Calibration is needed to define unknown or
uncertain parameters in the face of incomplete information
about the buildings being modeled. For example, Sokol
et al. (2017) developed a Bayesian methodology to calibrate
the parameters of building archetypes using measured energy
data. Probabilistic representation was used for parameters
with limited or no information, and distributions were
updated to a posterior joint distribution that was more
representative of the district. While computational cost might
be a concern for such applications of Bayesian calibration
to UBEM, other studies mentioned above show a successful
reduction of this cost by integration of Bayesian calibration
with simplified meta-models. Such advances make Bayesian
calibration a promising approach for urban-scale modeling.
4. Modeling building operations, controls, and retrofits
From a life cycle point of view, most of a building’s energy
is consumed during its operational phase; thus, it is crucial
that building simulation tools be applied during this phase
to identify and evaluate impactful energy-saving technologies
and strategies. Moreover, in developed countries the buildings
sector is dominated by existing buildings. Accordingly,
improvement of existing building operation and controls
and existing building retrofits are key strategies for reducing
the overall energy use of the buildings sector. Three
important use cases of building simulation in the building
operation, control and retrofit phases are presented as
Energy retrofit analysis
Detailed energy models created using BPS programs can be
used to explore and evaluate energy conservation measures
Hong et al. / Building Simulation
(ECMs) for energy retrofit projects. Usually, the basecase
model is calibrated using monthly utility bill data
before being used to simulate and analyze ECMs. Recent
developments in this area include web-based platforms or
toolkits that enable easy-to-use energy retrofitting analysis,
which in turn informs ECM selection.
For example, in the commercial sector CBES (Hong et al.
2015a) is a web-based energy retrofit analysis toolkit for
small-to-medium-sized commercial buildings in California.
The tool provides energy benchmarking and three levels
of retrofit analysis considering the project goal, data
availability, and user experience. The three levels of retrofit
analysis are: (1) smart meter data analytics to derive and
benchmark electric load to identify no or low-cost operation
improvements (Luo et al. 2017), (2) a lookup table style
query of ECMs using building high-level information
and a pre-simulated large database (Lee et al. 2015), and (3)
detailed energy modeling and a pattern-based calibration
method (Sun et al. 2016) to evaluate retrofit ECMs. CBES
currently offers 82 ECMs for lighting, envelope, plug-in
equipment, HVAC, and service hot water retrofit upgrades,
using OpenStudio and EnergyPlus to create and run energy
models. An extended version of the tool, CBESPro, covers
all U.S. climate zones. Regnier et al. (2018) demonstrated
the use of EnergyPlus models to assess ECMs for retrofitting a
building in Hawaii. Emphasis is on the integrated systems
approach to consider all related energy systems and their
integrative effects for deep energy retrofit of buildings.
On the residential side, Home Energy Saver™ (HES)
empowers homeowners and renters to save money by
reducing energy use in their homes. HES recommends
energy-saving upgrades that are appropriate to each home
and make economic sense given the home’s climate and
local energy prices. HES also estimates the home’s carbon
footprint and shows how much this footprint may be
reduced by energy-saving upgrades. For the urban scale
energy retrofit analysis, more tools are emerging; for example,
CityBES (Hong et al. 2016a) is a web-based data and
computing platform for large-scale energy retrofit analysis
of hundreds or thousands of buildings in a city district or
entire city (Chen et al. 2017).
Model-based retro-commissioning
Most buildings do not perform as well in practice as intended
by design, as their energy performance levels deteriorate
over time. Reasons for this deterioration in performance
include faulty construction, malfunctioning equipment,
incorrectly configured control systems and inappropriate
operating procedures. One approach to addressing this
problem is to compare the predictions of an energy simulation
model of the building to the measured performance and
analyze significant differences to infer the presence and
location of faults, a topic that is discussed further in the
next section. Model-based retro-commissioning refers to
this use of building energy models to help identify and
evaluate operation problems in buildings as part of a retro-
commissioning process. Calibrated energy models can be a
good tool in assisting the measurement and verification
(M&V) of a retro-commissioning project. As an example,
Marmaras (2014) discussed how building energy models can
be used in the retro-commissioning process of an under-
performing LEED Gold-level certified police station.
Real-time optimization, control, and fault detection and
Building control systems are critical to ensuring efficient
operations and occupant comfort. To support building
control, BPS is being coupled in real time with building energy
monitoring and control systems (EMCS) and sensors,
where it is used to predict thermal loads in buildings and
provide guidance on energy- and comfort-optimal control
strategies (e.g., set point adjustments, charging and discharging
of energy storage, demand response strategies).
Typically, real-time building operation data (equipment
and systems), predictive weather data, and occupant data
are fed to energy models that simulate and evaluate various
control strategies across a future time horizon, identifying
the control strategy with the best predicted energy and
comfort outcomes. This type of model predictive control
(MPC) is an advanced method of process control that has
been in use in chemical plants and oil refineries since the
1980s, only recently being appropriated for power system
balancing models and building controls (Morari and Lee
1999; Salakij et al. 2016). Model predictive controllers rely
on dynamic models of the process, most often linear
empirical models obtained by system identification. The
main advantage of MPC is the fact that it allows the current
operation timeslot to be optimized while simultaneously
accounting for future timeslots.
In an early example, Cumali and Sezgen (1989) introduced
a control optimization approach in a high rise office complex,
solving nonlinear equations that represent the building
environmental system in real time to identify optimal
control strategies, which were subsequently implemented
through an EMCS.
More recently, a research project (Piette et al. 2016; Blum
and Wetter 2017) under the joint U.S.-China Clean Energy
Research Center (CERC) is developing and demonstrating
a hierarchical, occupancy-responsive MPC framework
that optimizes the operation of buildings and campuses by
controlling lighting levels, HVAC operation, indoor air
temperature and humidity, indoor environmental quality,
window opening, and shading devices. The framework takes
into account anticipated weather, occupancy, price signals
Hong et al. / Building Simulation
from the electrical grid or district heating/cooling networks
and active and passive measures to store energy and reduce
peak loads. The proposed occupancy-responsive MPC
technology seamlessly integrates building technologies,
controls, and human behavior—a substantial need for
zero-net-energy and grid-responsive buildings.
Real-time BPS can also be used to detect and diagnose
faults (FDD) in buildings, a topic that is covered in more
detail in the next section. For example, Pang et al. (2012)
introduced a framework for simulation-based real-time
whole building performance assessment. The framework
allows comparison of actual and expected building perfor-
mance in real time using EnergyPlus, the Building Controls
Virtual Test Bed (BCVTB) and an Energy Management
and Control System (EMCS). Here, an EnergyPlus model
determines and reports the expected performance of a
building in real time; the BCVTB provides the software
platform for acquiring relevant inputs from the EMCS
through a BACnet interface; and these inputs are sent to
EnergyPlus as well as a database for archiving. Pang et al.
(2016) updated the framework to use the open functional
mockup interface (FMI) standard. In a separate study,
Bonvini et al. (2014) introduced a robust online FDD for
HVAC components based on nonlinear state estimation
Going forward, key challenges to the use of building
simulation in improving building operation, control, and
retrofit decisions include: (1) the need to create a detailed
physics-based energy model of a building in cases with
limited data availability (e.g., when as-built drawings and
specifications or records of building changes are not
available or are in a form that can be easily used), (2) the
lack of training data for data-driven or reduced-order
models, (3) the need to execute simulation models in real-
time, requiring computationally fast processes for data
collection, communication, and model execution, and (3) the
lack of BPS expertise and/or technical resources among
building operation staff or energy managers. Regarding
the latter challenge, the use of BPS in model-based retro-
commissioning or real-time control requires specialized skills
that are still new to the building simulation community—
particularly practitioners. Improved practitioner education
and pilot demonstration projects are needed to promote
and scale up such applications in real-world settings.
5. Modeling operational faults in existing buildings
Operational faults are common in existing buildings, leading
to decreased energy efficiency and occupant discomfort.
It is estimated that poorly maintained and improperly
controlled HVAC equipment is responsible for 15% to
30% of energy consumption in commercial buildings. Most
buildings, especially those with complex building energy
systems, have various degrees and types of operational
problems. Mills et al. (2005) analyzed 85 retro-commissioning
projects of existing buildings and found a total of 3,500
deficiencies, 11 per building. Correcting these deficiencies
through retro-commissioning proved to be cost effective.
It is reported that the number of maintenance requests
for building energy systems has increased exponentially
throughout the past decades, indicating an increase in
building operational faults (Cotts et al. 2010). Typical
operational faults may come from improper installation,
equipment degradation, sensor offset or failures, or control
logic problems. Such faults can be grouped into several
categories, including: (1) control faults, (2) sensor offset,
(3) equipment performance degradation, (4) fouling faults,
(5) stuck faults, and (6) others (Cheung and Braun 2015).
Figure 4 (Zhang and Hong 2017) illustrates some common
faults in a typical variable air volume (VAV) system with a
central plant.
HVAC operational faults may lead to a considerable
discrepancy between actual HVAC operation performance
and design expectations (Djuric and Novakovic 2009;
Karaguzel et al. 2014; Wang and Cui 2005). A series of
questionnaire surveys and interviews conducted by Au-Yong
et al. (2014) show the significant influence of poor HVAC
operation on occupant comfort, and some maintenance
factors are identified that are significantly correlated with
occupant satisfaction.
Simulating HVAC operational faults allows for an
estimation of the severity of common faults and thus supports
decision making about timely fault corrections, which can
then enable efficient system operation, improve occupant
thermal comfort, reduce equipment downtime, and prolong
equipment service life (Comstock et al. 2002; Wang et al.
Fig. 4 Potential operational faults in a typical VAV system with a
central plant (Zhang and Hong 2017)
Hong et al. / Building Simulation
2013). Such modeling can also support commissioning
efforts by providing estimates for potential energy/cost
savings that could be achieved by fixing the faults during
Quantified information on the impacts and priorities of
various coexisting operational faults can be provided to the
commissioners or the building management system, resulting
in more reasonable and reliable commissioning decisions,
especially when budget and staff resources are limited.
Moreover, modeling operational faults is critical to achieving
more reliable energy model calibrations, as most energy
models for existing buildings assume ideal conditions without
any operational problems. Specifically, this ability to estimate
the severity of common faults is expected to improve the
accuracy and transparency of the calibrated model, thereby
increasing the analysis accuracy of different retrofit
measures (Lam et al. 2014; Hong et al. 2015a).
Various FDD methods have been developed for HVAC
operational faults at the component or subsystem level.
Cheung and Braun (2015, 2016) developed fault models for
a variety of typical building energy system equipment with
three modeling techniques: empirical modeling, semi-
empirical modeling, and physical modeling. Radhakrishnana
et al. (2016) investigated the various constraints of HVAC
scheduling and proposed a novel, token-based distributed
control/scheduling approach that can account for varying
indoor environment and occupant conditions. Zhao et al.
(2013) proposed a pattern recognition-based method to detect
and diagnose faults in chiller operations, using a one-class
classification algorithm. Li et al. (2016a) also investigated
chiller operational problems, but with a two-stage, data-driven
approach based on linear discriminant analysis. Cai et al.
(2014) developed a novel method to analyze the faults of a
ground-source heat pump. Cai’s model achieves multi-source
information, fusion-based fault diagnosis by deriving Bayesian
networks from sensor data. Han et al. (2012) proposed an
automated fault detection and diagnosis strategy for vapor-
compression refrigeration systems, combining principle
component analysis feature extraction and a multiclass
support vector machine classification algorithm. The
operational faults of several other major HVAC components
have also been investigated, such as air handling units
(Du and Jin 2008; Gao et al. 2016; Najafi et al. 2012), heat
exchangers (Palmer et al. 2016), and fan coil units (Lauro
et al. 2014).
Existing methods for fault detection and diagnosis
generally fall short of holistically predicting the overall impacts
of faults at the building level—an approach that addresses
the coupling between various operational components, the
synchronized effect between simultaneous faults, and the
dynamic nature of fault severity. One recent advance in this
area is the addition of new features to EnergyPlus to model
HVAC operational faults and simulate their impact on
energy use and occupant comfort in buildings (Zhang
and Hong 2017). With this addition, EnergyPlus can now
represent the whole building impacts of sensor faults
(temperature, humidity, pressure, and enthalpy), faults in
thermostat and humidistat offsets, economizer damper
faults, and the fouling of air filters, coils, cooling towers,
chillers, boilers and evaporative coolers.
Modeling operational faults remains a challenge due to
an inadequate understanding of the complexity and dynamic
nature of faults and limitations in the measured operational
data from real building systems and equipment. Going
forward, state-of-the-art experimental facilities such as LBNL’s
FLEXLAB (Lawrence Berkeley National Laboratory 2018)
can be leveraged to generate new data to develop, test and
benchmark fault modeling and simulation capabilities in
tools like EnergyPlus. Key technical challenges include the
development of simple yet robust fault models, the collection
of high-quality data to represent fault characteristics, and
the integration of physics-based and data-driven methods
or models. As larger volumes of data from sensors, meters,
energy monitoring and control systems and IoT devices
in buildings become available, advanced data analytics,
machine learning, and hybrid modeling techniques can be
used to extract valuable information for the development
and application of novel fault modeling and simulation
approaches in BPS programs.
6. Zero-net-energy buildings and grid-responsive buildings
Zero-net-energy buildings
Zero-net-energy (ZNE) buildings, also named net-zero-
energy buildings, refer to buildings that are self-sufficient
with on-site energy production meeting their energy con-
sumption needs on an annual basis. The definition of ZNE
buildings may vary depending on what energy performance
metrics are used (U.S. Department of Energy 2015a), for
example, annual site (final) energy use and annual source
(primary) energy use. In the U.S. and Canada, the number
of ZNE buildings is on the rise. New Building Institute’s
Getting to Zero database (New Building Institute 2018) lists
nearly 500 certified, verified and emerging ZNE buildings
projects, reflecting a steep curve upward with the count
increasing over 700% since 2012.
There are a number of long-term advantages of moving
toward ZNE buildings, including lower environmental
impacts, lower operating and maintenance costs, better
resiliency to power outages and natural disasters, and
improved energy security. Reducing building energy
consumption in new building construction or renovation
can be accomplished through various means, including
Hong et al. / Building Simulation
integrated design, energy efficiency retrofits, reduced plug
loads and energy conservation programs. Reduced energy
consumption makes it simpler and less expensive to meet the
building’s energy needs with renewable sources of energy.
In its long-term energy efficiency strategic plan, California
targets ZNE buildings for all new residential construction
by 2020 and all new commercial construction by 2030. The
ZNE goals will play a significant role in regulatory agency
and utility efforts to promote the achievement of the state’s
greenhouse gas reductions goals. ZNE buildings usually adopt
energy efficiency technologies and advanced operation and
controls to reduce energy demand as much as possible first,
then generate on-site renewable power with solar PV or wind
turbines. The Road to ZNE (Heschong Mahone Group
2012), lists loading order or “steps to ZNE buildings”
including: (1) minimizing building loads, (2) optimizing
system efficiency based on equipment efficiency and use,
(3) using highest efficiency appliances, (4) optimizing building
operations to better meet occupant and energy efficiency
needs, (5) improved occupant interactions with the building,
and (6) renewable power generation when feasible.
Integrated design approaches and dynamic controls
(Arup 2012) are usually adopted for ZNE buildings that
optimize energy performance at the system and whole-
building levels considering interactions between all energy
end-use systems including envelope, HVAC systems,
lighting, plug-loads, and domestic/service hot water. In a
recent book, Eley (2016) identifies the building types and
climates where meeting the ZNE goal will be a challenge
and offers solutions for these special cases. One critical
challenge is to balance the operation of energy efficiency
technologies on the demand side and renewable generation
on the supply side.
Evaluation and optimization of ZNE design and operation
strategies varies for each ZNE project and cannot be done
using general rules-of-thumb. Supporting such case-by-case
analysis, BPS provides a quantitative evaluation of design
alternatives with various levels of complexity to inform
decision making. Modeling passive and advanced interactive
control strategies (Shen and Hong 2009; Hong and Fisk 2010)
for ZNE buildings remains a challenge for BPS programs
and users, e.g., natural ventilation, effective use of thermal
mass, precooling, phase change materials, radiant cooling/
heating systems, dynamic facades integrating needs of
daylighting and shading, zonal HVAC systems enabling
individual zone on-demand controls (e.g., VRF systems,
Hong et al. 2016b), and smart devices managing plug-loads
based on occupancy and use.
Grid-responsive buildings
Until recently, buildings have been regarded as pure energy
consumers of grid electricity. However, with on-site electricity
generation from solar PV and other renewable sources,
buildings are now able to produce more electricity than
they consume and can feed surplus energy to the grid—e.g.,
buildings are becoming prosumers rather than consumers.
Accordingly, from the perspective of electricity flows, the
building-to-grid relationship is moving in two directions.
Additionally, with increased deployment of intermittent
distributed energy resources like solar PV and wind turbines
grid capacity is becoming more variable and uncertain.
Grid-responsive buildings are those that can adjust
electricity demand and on-site energy generation based on
the dynamic needs of the grid. The ways and means of such
grid-responsiveness are found in increased deployment of
IoT devices and equipment and human-in-the-loop feedback
control strategies. The resulting flexibility in building
electricity demands help to avert system stress, enhancing
the reliability of the entire power grid.
The design and operation of grid-responsive buildings
is challenging because energy cost may be valued differently
depending on fast-changing grid conditions. Use of various
types of energy and electricity storage is key to serving
critical loads in such buildings, which must also be able to
operate in partial services modes both in time and space.
Rapidly coordinating demand and supply from groups of
buildings is necessary for smooth grid operation and security.
Viewed from the perspective of BPS tool development,
a key challenge is coupling traditional building energy
simulation with simulation of renewable energy generation
and the utility grid, where the temporal and spatial fidelity
of such models can be dramatically different.
Recently, the U.S. Department of Energy’s (DOE) Building
Technologies’ Office (BTO) launched a grid-interactive
efficient buildings (GEB) initiative to promote research
on technologies and policies that enable buildings to be
responsive and dispatchable in response to grid needs
(Nemtzow 2017). The BTO GEB initiative works closely
with DOE’s broader Grid Modernization Initiative (GMI),
a comprehensive effort with public and private partners
across different DOE offices and national laboratories to
help shape the future of our nation’s grid.
Energy-positive buildings and zero-net-emission buildings
As buildings become more grid-responsive, the potential
for energy-positive and carbon-neutral buildings is also
emerging. Such buildings employ advanced technologies,
including: building-integrated PV, direct-current driven
appliances and HVAC equipment, electric batteries, thermal
energy storage, smart thermostats, occupant-based controls,
and electric space heating, hot water heating, cooking and
drying (e.g., via heat pumps). Modeling these technologies
Hong et al. / Building Simulation
interactively poses a particular challenge for BPS applications.
In these advanced cases, accurately representing both
the technologies and behavioral opportunities may be
beyond the native modeling capabilities of BPS programs.
Accordingly, BPS programs must be flexible to the addition
of expanded modeling capabilities—e.g., by coupling with
other simulation programs. BPS programs like EnergyPlus
already allow users to write custom computer code using
an Energy Management System feature, which can enable
new control models and/or overwrite existing algorithms
for the program. However, such use requires advanced user
experience and deep knowledge of a particular BPS program.
Coupling BPS with Modelica-based equation-type modeling
tools (Wetter 2009; Wetter et al. 2015b) is another possibility
that evidences greater modularity and flexibility in meeting
such advanced modeling needs.
7. Urban building energy modeling
In cities, buildings are responsible for up to 70% of total
primary energy use. Energy conservation and efficiency
improvements constitute a key strategy for achieving cities’
energy and climate goals. To support such improvements,
cities and their consultants need urban building energy
modeling and analysis tools that combine measured data,
physics-based and data-driven models to inform urban
energy planning as well as to guide building retrofits at scale.
While there is currently no common definition of
urban building energy modeling (UBEM), UBEM usually
refers to computational simulation of the performance of a
group of buildings in an urban context (from a city block to
a district to an entire city) to account for the dynamics of
individual buildings and, more importantly, inter-building
effects that are coupled with the urban microclimate,
providing quantitative insights for urban planning and
energy policy making. The concept of urban building per-
formance includes individual building energy performance,
occupant comfort, district energy systems, as well as building
on-site or community-scale renewable power generation
and storage systems.
Accurately representing the urban microclimate con-
stitutes a key challenge for UBEM. The urban microclimate
is determined by: (1) local air velocity, temperature and
humidity; (2) solar irradiation and specular and diffuse
reflections; and (3) surface temperatures of buildings, the
ground and the sky, with the respective long-wave radiant
exchange between surfaces. While the urban environment
has strong influences on building thermal loads, operation
strategies (e.g., natural ventilation), on-site renewable power
generation (e.g., solar PV), and energy and occupant
comfort, buildings also influence the urban environment
(for example, buildings emit air and heat to the surrounding
urban context). UBEM captures these interactions between
buildings and the urban microclimate, and can represent
on-site renewable energy generation as well as district energy
systems that serve a group of buildings, taking advantage
of their thermal load diversity and the potential for heat
recovery between buildings. Considering urban buildings
as part of whole urban systems (a system of systems) enables
greater performance improvements than would be possible
given independent consideration of individual buildings.
Increasingly, UBEM tools are becoming available with
diverse fidelity and requirements of computational resources
and user inputs. Recent examples (Keirstead et al. 2012;
Reinhart and Davila 2016) include the Urban Building
Energy Models (UBEM) (Reinhart and Davila 2016; MIT
Sustainable Design Group 2016), the Urban Modeling
Interface (UMI) (Reinhart et al. 2013), CitySim (Emmanuel
and Jérôme 2015), UrbanOpt (National Renewable Energy
Laboratory 2018), and the City Building Energy Saver
(CityBES) (Hong et al. 2016a; Chen et al. 2017). Each tool
is further described here.
UBEM estimates citywide hourly energy demand from
energy simulations of individual buildings in a city,
supporting city policy makers to evaluate strategies on
urban building energy efficiency. UMI is a Rhino-based
design environment for architects and urban planners
interested in modeling the environmental performance of
neighborhoods and cities with respect to operational and
embodied energy use, walkability and daylighting potential.
UMI creates EnergyPlus models using simplified zoning
and HVAC systems. CitySim uses its own XML schema to
represent building information and a reduced order energy
models assuming simplified zoning and HVAC systems.
UrbanOpt is an analytics platform for high-performance
buildings and energy systems within one geographically
cohesive area in a city. UrbanOpt uses Openstudio and
EnergyPlus to model and evaluate city district planning
scenarios. Note that such tools are limited to specific
applications, and do not use open data standards, which
are key to sharing and exchanging information across a
wide array of urban modeling tools.
Another approach by KU Leuven and 3E uses the
Modelica-based framework developed for open Integrated
District Energy Assessment by Simulation (OpenIDEAS).
This approach employs building load profiles to optimize
district energy, leveraging Modelica libraries (Fuchs et al.
2015; Wetter et al. 2015b) and integrating physics-based
modules of systems in a larger context such as district
heating/cooling or shared energy infrastructures (Baetens
et al. 2012, 2015).
CityBES is an open web platform for simulating city
building energy efficiency. It provides: (1) a GIS-based
Hong et al. / Building Simulation
building performance visualization, (2) portfolio scale building
energy benchmarking, and (3) urban scale building energy
retrofit modeling, simulation and analysis. CityBES builds
upon open city datasets compiled in CityGML which is an
international OGC standard for representation and exchange
of 3D city models.
While UBEM programs such as CitySim and OpenIDEAS
employ reduced order energy models, others use physics-
based detailed energy models—e.g., UMI, UrbanOpt and
CityBES use EnergyPlus.
Recent advances in UBEM include new features added
to EnergyPlus version 8.8 to improve its use for UBEM,
including: (1) enabling import and export of external shading
results, (2) explicitly considering the long-wave radiant
exchange between buildings to address the urban canyon
effect, and (3) using urban microclimate conditions to
address the urban heat island effect.
Additionally, an exascale computing project (U.S.
Department of Energy 2018c), Multiscale Coupled Urban
Systems, is currently developing a data and computing
framework to couple building energy models (EnergyPlus),
urban climate models (WRF and NEK5000, National Center
for Atmospheric Research 2018; Argonne National Laboratory
2018) and transportation models (Transportation Utility
Management System 2018), and to quantify their inter-
dependencies to inform urban planning.
As hundreds or more buildings are involved in a typical
urban building energy modeling application, automatic
integration of data and simulation tools in a seamless
workflow with high-performance computing capabilities
remains a challenge for users. Specific issues include:
a. Big data: as large amounts of operational data (at the
terabyte scale) become available from buildings and cities,
significant effort is needed to quality control the data and
integrate them into models and standards that support
interoperability across diverse urban analysis tools and
b. Modeling and simulation: the interdependencies of
city sectors must be further studied by coupling urban
system models at various spatial and temporal resolutions,
encompassing buildings, the urban microclimate and
c. Computing: UBEM may constitute an exascale com-
puting problem that requires next-generation supercomputers.
For example, consider the computing that would be required
to run millions of building energy models representing
the City of New York in a reasonable time frame (say, up to
one hour).
d. Workflow: GIS-based visualization of UBEM results
is needed to ensure that stakeholder easily understand key
takeaways, such that UBEM models can meaningfully inform
decision making in a seamless workflow.
8. Modeling the national or regional impacts of building
energy efficiency
Moving beyond the urban scale, BPS is relevant to regional
and national modeling efforts as well. Indeed, federal and
state policy efforts to drive long-term reductions in energy
use and CO2 emissions through building energy efficiency
require quantitative representations of the national or regional
building stock and its energy use under future scenarios of
technology deployment. Such building efficiency impact
models integrate three classes of information: (1) national
building and technology stocks and their change over time;
(2) the energy use intensities of installed building equipment,
envelope components, and operational routines; and (3) the
likelihood of consumer or organization choices to adopt
new technologies or operational strategies, or to replace or
retrofit existing technologies.
Existing models of the building stock segment buildings
by geographic location and physical characteristics (e.g.,
size, vintage, program type) and apply functions for annual
additions and demolitions in each stock segment in order
to make projections (Energy and Environmental Economics
(E3) 2016; U.S. Department of Energy Building Technologies
Office (BTO) 2017; U.S. Energy Information Administration
2017a). Installed bases of equipment in each segment are
concurrently represented along with flows into and out of
equipment stocks over time as technologies are replaced
upon burnout or retrofitted. The overall energy use intensity
of a particular segment depends on the rate of turnover
in its installed equipment base, the unit-level energy
performance level of installed equipment, and physical
improvements to the building structure. Here, building-
level energy use may be represented implicitly or explicitly
by statistical or engineering models, discussed further below.
New, more efficient equipment or building components
penetrate the installed base over time based on technology
choice assumptions, which are often driven by economic
considerations about the cost of purchasing and operating
the new technologies over the course of their useful lifetimes
(e.g., Wilkerson et al. (2013) and see next section on
technology adoption modeling).
National and regional-scale impact models are challenged
by the large scale of the modeled phenomena over time and
space, which leads to difficulties in collecting and updating
the data needed to specify a model with a high degree of
geographic granularity and in developing model validation
methods for outcomes that span several decades into the
future. Regarding the data collection challenge: in the
United States there are 125 million homes and 6 million
commercial buildings, each of which has a unique equipment
inventory, construction characteristics, occupant population,
and energy use intensity (U.S. Energy Information
Hong et al. / Building Simulation
Administration 2016, 2017d). Collecting data that are
sufficiently representative of this heterogeneous building
population and comprehensive enough to inform national-
scale stock and energy models requires a robust measurement
protocol and substantial buy-in from the building owners
who will be asked to provide these data.
From a modeling perspective, efforts to validate the
outputs of national or regional impact assessments confront
the impossibility of evaluating predictions that extend
decades into the future. As a result, key dynamics in the
model, such as rates of equipment turnover, technology
market penetration rates, and changes in energy prices, are
extrapolated from historically-derived relationships (Koomey
2000). When historical data are not available, such assumptions
may be based on educated guesses by the modeler and/or
expert elicitation. Absent well-established protocols for
reporting and validating these assumptions, models are
limited to exploratory rather than explanatory and/or
predictive use cases. Nevertheless, these model validation
issues are rarely highlighted, leading to the incorrect treatment
of simulated results as predictions about the future rather
than rough indicators of impactful strategies for long-term
energy and CO2 curtailment.
The importance of developing high-quality building
efficiency impact models lies in the use of such models to
frame high-profile energy policy decisions. Indeed, models
of national scale energy demand have been used to evaluate
participation in international climate agreements (U.S.
Energy Information Administration 1998), develop and assess
energy use and emissions reduction targets (Williams et al.
2012b), and craft technology R&D strategies that are likely
to yield long-term energy savings cost-effectively (Farese et
al. 2012). Moreover, such models may be useful outside the
policy context—for example, for utilities designing building
efficiency program measures and incentives, or for businesses
seeking to anticipate future trends in the building efficiency
Recent progress in building efficiency impact modeling
can be grouped into top-down and bottom-up studies (Lim
and Zhai 2017b). In top-down studies, historical relationships
are derived between aggregate-level energy use and macro-
economic indicators (e.g., gross domestic product, price
indices), climatic conditions, appliance ownership, and
housing stock turnover rates. Top-down approaches benefit
from their simplicity and reliance on widely available
historical data; however, energy use scenario projections
are strongly dependent on historical trends, and the lack
of end use- or technology-level energy use disaggregation
precludes the assessment of impacts for specific ECMs.
Example top-down models include the Global Climate
Change Assessment Model (GCAM) (Joint Global Change
Research Institute 2017).
By contrast, bottom-up modeling studies use statistical
or engineering models to explicitly represent energy end
uses at the building level along with key determinants of
energy use (e.g., climate, equipment, occupancy, building
shell characteristics). Sector-level energy use projections
are then developed from stock- or floor area-weighted
combinations of the energy use calculated for multiple
building types. Given their greater degree of energy use
disaggregation, bottom-up models allow the direct assessment
of ECMs; however, they require more data to develop than
top-down models, and may also be more complex. Example
bottom-up models include the EIA National Energy Modeling
System (NEMS) (U.S. Energy Information Administration
2017b,c) Scout (U.S. Department of Energy Building
Technologies Office (BTO) 2017), ResStock (National
Renewable Energy Laboratory 2017), and EnergyPATHWAYS
(Energy and Environmental Economics (E3) 2016).
Ongoing building stock and energy data collection efforts
include the U.S. EIA Residential Energy Consumption Survey
(RECS) (U.S. Energy Information Administration 2017d)
and Commercial Building Energy Consumption Survey
(CBECS) (U.S. Energy Information Administration 2016),
which have been conducted on a nationally representative
sample of residential and commercial buildings roughly
every four years since the late 1970s. RECS and CBECS
data collection includes building characteristics, appliances
and equipment, demographics, and energy use. The U.S.
Department of Energy’s Building Performance Database
(U.S. Department of Energy 2018d), which contains records
on the energy-related characteristics of over one million
buildings, provides another source of large-scale building
energy use data, though these data are not yet nationally
representative. In the European Union, a Building Stock
Observatory (European Commission–Energy 2018) was
recently launched that aggregates national-level studies of the
building stock from 20 member countries and establishes a
plan for continuous data updating in the future; the data
track a similar set of variables to RECS and CBECS.
Future work should explore innovative methods for
large-scale stock and energy data collection. By pairing
machine learning techniques with GIS data, for example,
the physical characteristics of a national-scale building
stock can be determined without the need to conduct costly
in-person assessments, as is currently done for RECS and
CBECS. Moreover, the burden of building owner surveys
can be reduced by conducting the surveys online and
pairing responses with readings from advanced metering
infrastructure (U.S. Energy Information Administration
2015). In parallel with these advances in data collection
methods, advances are needed in methods for organizing
and sharing available data. For example, existing platforms
like the U.S. Department of Energys Building Energy Data
Hong et al. / Building Simulation
Specification (BEDES) (U.S. Department of Energy 2018e),
which serves as a buildings data dictionary, should be explored
as common standards for building stock and energy data
On the modeling side, protocols must be developed
to improve the transparency of impact model elements,
development, and validation. While recent modeling progress
mostly concerns national-scale analyses, certain tools claim
flexibility in extending to regional or state-level analyses,
given regionally- or state-specific input data. Without a clear
description of model elements, however, development of
custom input datasets that are compatible with the model is
a substantial burden for utility or state energy analysts.
Indeed, available building efficiency impact models range
in their geographical scale of applicability, input variable
types, and implementation. Description guidelines akin to
the ODD protocol (Grimm et al. 2010), which is used to
compare agent-based models across disciplines, will help
structure a comparison of these disparate impact modeling
options. Such protocols can also improve the understanding
of approaches to model validation and uncertainty
quantification, which are currently not widely published.
Recently, a new IEA EBC Annex was launched
(International Energy Agency (IEA) Energy in Buildings
and Communities Programme 2018) that seeks to support
many of these future research tasks. Specifically, Annex 70
proposes the epidemiological study of large-scale energy
demand, which will inform models that estimate changes
in this demand due to energy efficiency and occupant
behavior measures. The Annex places a particular focus on
cataloging existing datasets and models and establishing
best practices for new data collection and modeling efforts.
9. Modeling the adoption of energy efficient technologies
As the previous section suggests, forecasts of the regional
or national energy and CO2 emissions reduction potential
of building efficiency technologies depend on the assumed
rates at which the technologies diffuse into targeted segments
of building energy use. These rates stem on one hand
from technology stock-and-flow dynamics— rates of new
construction, retrofits, and replacement, for example—and
on the other hand from the behavioral dynamics of consumer
or organization technology adoption decisions. Yet, little
research has been devoted to developing building technology
adoption models and studying their application to forecasts
of future building energy use.
Efforts to model building technology adoption decisions
are challenged by the broad array of potential adoption
drivers and constraints, which may vary by adoption decision
type (U.S. Energy Information Administration; 2017b),
adopter type (Rogers 1995), and technology type (Jaccard
and Dennis 2006). Examples of such variables include:
adopter preferences, perceptions of technology attributes,
the availability of capital and expertise to implement the
technology, social influences, demographic, political, and
economic trends, and external constraints on technology
Several modeling frameworks may be used to explain
and/or predict technology adoption outcomes (Gilshannon
and Brown 1996; Raju and Teotia 1985; Packey 1993).
Indeed, adoption model types range from simple historical
analogy approaches, where the market penetration of a new
technology is mapped to the historical shares of a similar,
existing technology, to agent-based approaches, where
adoption decisions are explicitly represented at the level of
individual adopters. Nevertheless, little guidance exists on
which modeling framework should be chosen for a particular
use case.
Additionally, all of these modeling approaches require
some degree of supporting data on historical market sales,
technology characteristics, adopter characteristics, and/or
societal trends; yet, relevant datasets are sparsely organized
and differ in their degree of relevance, representativeness,
recurrence, and richness (Ratcliffe et al. 2007). Even if these
existing data were made more widely accessible and com-
prehensive, clear trends in technology adoption might take
several years or even decades to emerge, and past trends in
adoption may not hold for new or emerging technologies
that depart substantially from the features of historical
The importance of addressing such challenges through
future research is underscored by the clear influence that
building technology adoption assumptions have on the
outcomes of national-scale energy use projections. In the
U.S. Energy Information’s 2014 Annual Energy Outlook
(AEO) projections (U.S. Energy Information Administration
2017a), for example, a scenario that assumes adoption of
only the best available technologies yields a 20% reduction
in Reference Case building energy use by 2040. A study of
an earlier AEO version (Wilkerson et al. 2013) similarly
explored the effects of both more and less efficient technology
choice assumptions on Reference Case outcomes, finding
+11%/−14% sensitivities in projected energy use outcomes
by 2035. Similar analyses by IEA (International Energy
Agency 2017) further evidence the influence of technology
choice assumptions on projected energy use and CO2
outcomes, finding that these outcomes are more strongly
tied to modeled technology choices than to modeled
technology performance improvements.
Recent progress in modeling energy technology adoption
mostly concerns the areas of transportation and renewable
energy; however, the fewer buildings-focused studies that
do exist represent a wide range of modeling approaches.
Hong et al. / Building Simulation
On the simpler end of the spectrum, multiple Technical
Support Documents (TSDs) from DOE’s Appliance Standards
Program rely on time series and historical analogy models,
which project future equipment shipments based on average
historical market saturations for the technology in question
or—if these historical data are not available—on the
saturations for a similar technology with available historical
data (Navigant Consulting and Lawrence Berkeley National
Laboratory 2011; Navigant Consulting and Pacific Northwest
National Laboratory 2014, 2016).
Other studies have relied on diffusion modeling
approaches, assuming that the spread of a new technology
is driven by a process of innovation (“external influence”)
and/or imitation (“internal influence) (Buskirk 2014; Elliott
et al. 2004; Farese et al. 2012); cost models, where technology
market shares are projected based on tangible costs (e.g.,
capital cost, operating costs) and intangible costs (e.g.,
perceived changes to comfort and system responsiveness)
(Jaccard and Dennis 2006; U.S. Energy Information
Administration 2017b; Weiss et al. 2010), econometric and
discrete choice models, where a functional relationship is
developed between technology market share and one or
more influencing variables (Andrews and Krogmann 2009;
Higgins et al. 2014; Kok et al. 2012; Li 2011; Navigant
Consulting and Lawrence Berkeley National Laboratory
2014; Noonan et al. 2013; U.S. Energy Information
Administration 2017c); and system dynamics and agent-
based models where causal mechanisms behind adoption
behavior are explicitly represented at the aggregate or
individual adopter level (Lee et al. 2014; Moglia et al. 2017;
Muehleisen et al. 2016; Müller 2013; Nachtrieb 2013;
Navigant Consulting 2013; Sopha et al. 2013; Zhang and
Nuttall 2007).
The data requirements of the above modeling frameworks
range from historical market shares (time series, historical
analogy, and diffusion models) to perceived technology
attributes, individual-level adoption preferences and decision
weights, and contextual factors (discrete choice, system
dynamics, and agent-based models). Market share data are
available from DOE Appliance Standards TSDs (Navigant
Consulting 2017; U.S. Department of Energy Appliance
Standards Program 2016) EIA consumption surveys (U.S.
Energy Information Administration 2016, 2017d), ENERGY
STAR (2017), and several industry associations such as the
Consumer Technology Association (CTA) (2018), American
Heating and Refrigeration Institute (AHRI) (2017), and the
National Electrical Manufacturers Association (NEMA)
(2018). DOE, EIA, ENERGY STAR, and AHRI also offer
publicly available datasets on technology performance,
cost, and/or lifetime characteristics (American Heating and
Refrigeration Institute (AHRI) 2018; ENERGY STAR 2018;
Navigant Consulting 2016, 2017; U.S. Department of Energy
Appliance Standards Program 2016). Consumer and/or
organization data are collected through surveys, most notably
the Johnson Controls Energy Efficiency Indicator (Institute
for Building Efficiency 2016) and ENERGY STAR Awareness
Survey (EPA Office of Air and Radiation 2017). Data on
consumer demographics and larger social, economic, and
political trends may be obtained from the U.S. Census (U.S.
Census Bureau 2017a,b), U.S. Bureau of Economic Analysis
(BEA) (U.S. Department of Commerce 2017), and American
Council for an Energy Efficient Economy (ACEEE)
(ACEEE 2017).
Examined independently, existing building technology
adoption models and datasets exhibit a narrow focus on
one or a few technology types, predictor variables of interest,
or areas of application within the buildings sector; indeed,
each of these modeling approaches and datasets has unique
strengths and drawbacks. Near term research efforts must
accordingly focus on using the strengths of one model type
or dataset to mitigate the weaknesses of another. Parallel,
long term research efforts could then be dedicated to filling
the gaps that are most likely to remain after existing models
and data are merged.
Regarding models, areas for potential integration include:
using historical analogy models to select diffusion model
parameter coefficients for new technologies with little data;
using cost models and/or econometric models to provide long
range market share potential estimates for diffusion models;
and incorporating bottom-up agent adoption dynamics into
top-down system dynamics or equation-based models. Some
of these model combinations are already observed in the
buildings literature, albeit for a limited set of technology
types (e.g., see Farese et al. (2012); Higgins et al. (2012);
Jaccard and Dennis (2006); Navigant Consulting and Lawrence
Berkeley National Laboratory (2014)).
Similar opportunities for data integration are observed.
For example, in the residential sector, ENERGY STAR
historical shipments data may be cross referenced with
concurrent versions of the ENERGY STAR Awareness Survey,
the ENERGY STAR products database, Census demographics
data, and ACEEE energy policy environment data. In the
commercial sector, Johnson Controls EEI data on efficient
measure adoption, adoption barriers, and/or payback
preferences may be cross referenced with point-in-time
shipments and saturation data (e.g., from TSDs, CBECS,
ENERGY STAR). Emerging data sources such as Google
Trends and Correlate (Choi and Varian 2009; Google Labs
2011, 2018), Amazon Mechanical Turk (Amazon 2017),
and the Twitter API should be explored for their ability to
supplement these traditionally referenced databases.
Looking further ahead, new data collection efforts must
anticipate the gaps in key variables that will remain after
existing datasets are merged. Here, data on consumer or
Hong et al. / Building Simulation
organization decision preferences is expected to serve as an
important area of focus; these data can be generated through
large-scale discrete choice experiments (Henser et al. 2015)
that elicit parameter weights for explanatory models of
technology adoption.
Finally, protocols must be developed to guide the selection,
verification and validation, and communication of building
technology adoption models. Model selection should be
determined by the model’s use case, the simulated time
horizon, the scope of modeled technologies, and the level
of resources available for model development. Model
verification and validation must address the difficulty in
acquiring long-term technology market share data to validate
modeled outcomes against and emphasize the importance
of ground-truthing key input assumptions and variable
relationships (Koomey 2000). Model communication efforts
should seek to clearly describe model inputs, outputs, and key
relationships; the data sources used for model development
and validation; and the limitations inherent to the modeling
approach and data sources (for example, see Sopha et al.
(2013)). Given inevitable gaps in the data that are available
for building technology adoption model development and
validation, uncertainties in modeled outcomes should be
communicated through scenario analysis and quantified
where possible using formal statistical techniques.
10. Integrated building performance simulation
The preceding sections suggest a wide field of potential
applications for BPS. Ensuring the future flexibility and
robustness of BPS across these varied use cases will require
greater focus on integration activities across four dimensions:
(1) data, (2) domain, (3) tool, and (4) workflow. These
opportunities for BPS integration are described further
Data integration
During the building life cycle, BPS is used in various ways
from early design to detailed design to commissioning,
operation and controls to retrofit. Data from all available
sources should be integrated under the building information
modeling (BIM) framework, which enables the application
of one model across multiple simulation cases (Hong et al.
1997). Specifically, an energy model developed to inform
early design decisions can be refined as more data are made
available in the detailed design or operation phases, allowing
the model to inform decisions later on in the building life
cycle. In practice, such efforts are hindered by the lack of
regulations or policies that require new building projects to
submit BIM or energy models of the buildings. By result,
most BPS models are not standardized or shared among key
stakeholders. This leads to the time-consuming, error-prone,
and wasteful effort to recreate multiple models of the same
building for different purposes by different users.
Although BIM started decades ago to represent building
geometry data and simplified data of thermal loads in
buildings, it is still limited in representing HVAC systems,
occupant behavior or operational and control data. This
leads to problems with storing, managing and integrating
these other data sources. Current BIM is also limited in
representing simulation results at various levels of spatial
and temporal resolutions.
Domain integration
As BPS moves from its application to individual building
design and operation to the simulation of grid-responsiveness,
communities, and regional or national energy use, multiple
technical domains must be integrated such that their
interactive effects may be quantified, yielding more holistic
assessments across a diverse set of stakeholder needs.
Technical domains to be integrated include: (1) energy
efficiency of buildings, (2) occupant behavior of energy
use and human-building interactions, (3) energy storage,
(4) building operation controls, (5) renewable energy, on-site
or at the community scale, (6) demand response and grid-
responsive strategies, (7) indoor environmental quality
including thermal comfort, visual comfort and indoor air
quality, and (8) water use in buildings.
Simulation tool integration
Modeling and simulation efforts that span multiple technical
domains usually require the use of several different simulation
tools, which may cover building energy flows (e.g., EnergyPlus),
distributed energy resources (e.g., DER-CAM), CFD (e.g.,
FLUENT), grid conditions (e.g., the Integrated Grid Modeling
System IGMS), and human behavior (e.g., agent-based
modeling tool AnyLogic, obXML and obFMU (Hong et al.
2015b,c), Occupancy Simulator (Chen et al. 2018)). Various
approaches have been developed (Trčka et al. 2009; Wetter
2011) to couple cross-domain tools through co-simulation.
Co-simulation using the functional mockup interface and
functional mockup units shows particular promise: here,
two simulators solve coupled differential-algebraic systems
of equations and exchange data that couples these equations
during the time integration. Additionally, visualization of
the co-simulation process and results across simulation
tools is important for supporting design decision making.
Chen et al. (2018) provided an example of simulating and
visualizing occupant behavior and its impact on building
Workflow integration
Within the buildings industry, companies and consultants
each use their own workflows and suite of tools to support
Hong et al. / Building Simulation
decision making on buildings projects across the building
life cycle. Integrating new BPS programs with these existing
workflows and tools (e.g., CRM, finance tools, databases) is
a challenge from a business perspective. A particular issue
for new BPS programs is the need for data exchange and
interoperability with existing tools, such that no duplicate
data need be collected or re-entered for existing BPS
applications. Integrating BPS across stakeholders from
multiple firms (architects, engineers, energy consultants and
building owners) brings the additional challenge of data
privacy and IP ownership concerns. In this area, web-based
tools and the integration of web services for businesses are
becoming popular.
Summary and future perspectives
Over the past decade, building performance simulation
(BPS) has emerged as a crucial tool for the design and
operation of low energy buildings and communities. The
selected ten challenges aim to highlight some of the most
important technical needs currently facing BPS, covering
the full building life cycle and a wide range of modeling
scales of focus. The formulation and discussion of each
challenge aims to provide insights into the state-of-the-art
for the given topic and future research directions, and to
inspire new questions from young researchers in the field.
In addition to these research-level needs, several practical
barriers to BPS adoption and implementation warrant
further discussion here.
An overarching practical issue that most energy modelers
face is the time and effort required to collect adequate data
and develop reliable energy models. Detailed energy modeling
using today’s BPS programs requires many inputs, and
modelers may not have full knowledge of each input’s relative
importance to simulation outcomes, level of uncertainty,
and the appropriate default values to use (if not already
specified). This issue is exacerbated when actual or realistic
data (e.g., occupancy, operational schedules, infiltration)
are not available and the use of a typical input value or
assumption is not appropriate for the application case. The
issue can be addressed by developing new standards for
collecting and sharing input data for energy models—for
example, ASHRAE SPC 205, Standard Representation of
Performance Simulation Data for HVAC&R and Other
Facility Equipment.
Additionally, while BPS is often beneficial to use for
building design and operation, this is not always the case.
For example: when a project lacks the time, budget or
expertise to develop sound energy models; when a project
does not have the buy-in or support from key stakeholders
(e.g., building owners, architects and engineers); or when
rules-of-thumb and recent experiences are sufficient for
conventional design needs.
Finally, BPS programs that originate from sophisticated
research problems are only valuable in the long term if they
are of interest to a broad set of users. Regarding this point,
the aforementioned building performance gap is important
to address going forward, as it affects the perceived credibility
of BPS and weakens the justification for its widespread use
by building practitioners. This problem must be addressed
through a dedicated, interdisciplinary effort that engages
stakeholders spanning research, academia and industry.
In parallel, BPS value propositions must be communicated
amongst these stakeholders and reflected through building
codes and standards, rating schemes, policy and regulations.
Furthermore, best practices, education and training, and
professional certification programs for BPS practitioners
should be enhanced to highlight the quality and value of
BPS among its potential user base.
BPS is presently entering a new era of research and
application, given more affordable and powerful computing
resources and the rapid development of IoT, big data,
machine learning and artificial intelligence. In the future,
we believe BPS will provide unprecedented value to the design
and operation of low energy buildings and communities that
address timely issues of resource efficiency, environmental
sustainability, and resiliency in the built environment.
Under this vision, every new building will be virtually
designed and tested using building information modeling,
computational modeling and simulation, and virtual reality
technologies, and will be operated using augmented reality
and machine learning-driven predictive controls to achieve
ambitious energy performance goals.
This work was supported by the Assistant Secretary for
Energy Efficiency and Renewable Energy of the U.S. DOE
under Contract No. DE-AC02-05CH11231.
ACEEE (2017). The State Energy Efficiency Scorecard. Available at Accessed 17 Aug 2017.
Ahn KU, Kim DW, Park CS, de Wilde P (2017). Predictability of
occupant presence and performance gap in building energy
simulation. Applied Energy, 208: 1639–1652.
Amazon (2017). Amazon Mechanical Turk. Available at https:// Accessed 17 Aug 2017.
American Heating and Refrigeration Institute (AHRI) (2017). Historical
Data. Available at
Historical-Data.aspx. Accessed 15 Aug 2017.
American Heating and Refrigeration Institute (AHRI) (2018). AHRI
Certification Directory. Available at
ahridirectory/pages/home.aspx. Accessed 15 Aug 2017.
Hong et al. / Building Simulation
Andrews CJ, Krogmann U (2009). Explaining the adoption of energy-
efficient technologies in U.S. commercial buildings. Energy and
Buildings, 41: 287–294.
Andrade-Cabrera C, Turner WJN, Burke D, Neu O, Finn DP (2016).
Lumped parameter building model calibration using particle
swarm optimization. In: Proceedings of the 3rd Asia Conference
of International Building Performance Simulation Association
(ASIM 2016), Jeju Island, R.O. Korea.
Andrade-Cabrera C, Burke D, Turner WJN, Finn DP (2017). Ensemble
Calibration of lumped parameter retrofit building models using
Particle Swarm Optimization. Energy and Buildings, 155: 513–532.
ANSI/ASHRAE (2014). ASHRAE Guideline 14-2014. Measurement
of Energy, Demand, and Water Savings. Atlanta: American Society
of Heating, Refrigerating and Air-Conditioning Engineers.
Argonne National Laboratory (2018). NEK5000. Available at https:// Accessed 14 Jan 2018.
Arup (2012). The Technical Feasibility of Zero Net Energy Buildings
in California. Available at
Report_Final.pdf. Accessed 28 Jan 2018.
Au-Yong CP, Ali AS, Ahmad F (2014). Improving occupants’ satisfaction
with effective maintenance management of HVAC system in
office buildings. Automation in Construction, 43: 31–37.
Azar E, Menassa CC (2012). Agent-based modeling of occupants and
their impact on energy use in commercial buildings. Journal of
Computing in Civil Engineering, 26: 506–518.
Baetens D, De Coninck R, Jorissen R, Picard F, Helsen D, Saelens L
(2015). OpenIDEAS—An open framework for integrated district
energy simulations. In: Proceedings of the 14th International
IBPSA Building Simulation Conference, Hyderabad, India.
Baetens R, De Coninck R, Van Roy J, Verbruggen B, Driesen J, Helsen
L, Saelens D (2012). Assessing electrical bottlenecks at feeder
level for residential net zero-energy buildings by integrated system
simulation. Applied Energy, 96: 74–83.
Balaras CA, Dascalaki EG, Droutsa KG, Kontoyiannidis S (2016).
Empirical assessment of calculated and actual heating energy use
in Hellenic residential buildings. Applied Energy, 164: 115–132.
BEMbook (2018). History of Building Energy Modeling. Available at Accessed 20 Jan 2018.
Blum D, Wetter M (2017). MPCPy: An open-source software platform
for model predictive control in buildings. In: Proceedings of the
15th International IBPSA Building Simulation Conference, San
Francisco, USA.
Bonvini M, Sohn MD, Granderson J, Wetter M, Piette MA (2014).
Robust on-line fault detection diagnosis for HVAC components
based on nonlinear state estimation techniques. Applied Energy,
124: 156–166.
Bordass B, Cohen R, Field J (2004). Energy performance of non-domestic
buildings: Closing the credibility gap. In: Proceedings of the
International Conference on Improving Energy Efficiency in
Commercial Buildings.
BSRIA and UBT (2014). the Soft Landings Framework.
Burman E, Mumovic D, Kimpian J (2014). Towards measurement
and verification of energy performance under the framework of
the European directive for energy performance of buildings.
Energy, 77: 153–163.
Buskirk RV (2014). Estimating Energy Efficiency Technology Adoption
Curve Elasticity with Respect to Government and Utility
Development Program Indicators. Available at
Cacabelos A, Eguía P, Febrero L, Granada E (2017). Development of a
new multi-stage building energy model calibration methodology
and validation in a public library. Energy and Buildings, 146:
Cai B, Liu Y, Fan Q, Zhang Y, Liu Z, Yu S, Ji R (2014). Multi-source
information fusion based fault diagnosis of ground-source heat
pump using Bayesian network. Applied Energy, 114: 1–9.
Calì D, Osterhage T, Streblow R, Müller D (2016). Energy performance
gap in refurbished German dwellings: Lesson learned from a
field test. Energy and Buildings, 127: 1146–1158.
California Public Utilities Commission (2008). California Long Term
Energy Efficiency Strategic Plan. Available at
WorkArea/DownloadAsset.aspx?id=5305. Accessed 28 Jan 2018.
Chandra Putra H, Andrews CJ, Senick JA (2017). An agent-based
model of building occupant behavior during load shedding.
Building Simulation, 10: 845–859.
Chen Y, Hong T, Piette MA (2017). Automatic generation and
simulation of urban building energy models based on city
datasets for city-scale building retrofit analysis. Applied Energy,
205: 323–335.
Chen Y, Hong T, Luo X (2018). An agent-based stochastic occupancy
simulator. Building Simulation, 11: 37–49.
Cheung H, Braun JE (2015). Development of Fault Models for Hybrid
Fault Detection and Diagnostics Algorithm. Boulder, CO, USA:
National Renewable Energy Laboratory.
Cheung H, Braun JE (2016). Empirical modeling of the impacts of
faults on water-cooled chiller power consumption for use in
building simulation programs. Applied Thermal Engineering, 99:
Choi H, Varian H (2009). Predicting the Present with Google
Trends. Available at
Choi JH, Loftness V, Aziz A (2012). Post-occupancy evaluation of 20
office buildings as basis for future IEQ standards and guidelines.
Energy and Buildings, 46: 167–175.
Chong A, Lam KP, Pozzi M, Yang J (2017). Bayesian calibration of
building energy models with large datasets. Energy and Buildings,
154: 343–355.
Clarke JA (2001). Energy Simulation in Building Design. Oxford, UK:
Clarke JA (2015). A vision for building performance simulation: A
position paper prepared on behalf of the IBPSA Board. Building
Performance Simulation, 8: 39–43.
Clarke JA, Hensen JLM (2015). Integrated building performance
simulation: Progress, prospects and requirements. Building and
Environment, 91: 294–306.
Clevenger CM, Haymaker J (2006). The impact of the building
occupant on energy modeling simulations. In: Proceedings of the
Joint International Conference on Computing and Decision
Making in Civil and Building Engineering.
Hong et al. / Building Simulation
Coakley D, Raftery P, Keane M (2014). A review of methods to match
building energy simulation models to measured data. Renewable
and Sustainable Energy Reviews, 37: 123–141.
Comstock MC, Braun JE, Groll EA (2002). A survey of common
faults for chillers. ASHRAE Transactions, 108(1): 819–825.
Cotts D, Roperathy K, Payant R (2010). The Facility Management
Handbook, 3rd edn. New York: American Management
Consumer Technology Association (2018). Historical Sales Data.
Available at
Sales-Data.aspx. Accessed 16 Aug 2017.
Creswell JW (2006). The foundations of mixed methods research. In:
Creswell JW, Plano Clark VL (eds), Designing and Conducting
Mixed Methods Research. Thousand Oaks, CA: Sage Publications.
pp. 19–52.
Cui Y, Yan D, Hong T, Xiao C, Luo X, Zhang Q (2017). Comparison of
typical year and multiyear building simulations using a 55-year
actual weather data set from China. Applied Energy, 195: 890–904.
Cumali Z, Sezgen O (1989). Simulation and optimization in the real
time control of building environmental systems. In: Proceedings
of International IBPSA Building Simulation Conference, Vancouver,
Dasgupta A, Prodromou A, Mumovic D (2012). Operational versus
designed performance of low carbon schools in England:
Bridging a credibility gap. HVAC & R Research, 18: 37–50.
de Wilde P (2014). The gap between predicted and measured energy
performance of buildings: A framework for investigation.
Automation in Construction, 41: 40–49.
de Dear RJ (1998). Global database of thermal comfort field experiments.
ASHRAE Transactions, 104: 1141–1152.
Djuric N, Novakovic V (2009). Review of possibilities and necessities
for building lifetime commissioning. Renewable and Sustainable
Energy Reviews, 13: 486–492.
D’Oca S, Chen CF, Hong T, Belafi Z (2017). Synthesizing building
physics with social psychology: An interdisciplinary framework
for context and occupant behavior in office buildings. Energy
Research and Social Science, 34: 240–251.
D’Oca S, Hong T, Langevin J (2018). The human dimensions of energy
use in buildings: A review. Renewable and Sustainable Energy
Reviews, 81: 732–742.
Du Z, Jin X (2008). Multiple faults diagnosis for sensors in air handling
unit using Fisher discriminant analysis. Energy Conversion and
Management, 49: 3654–3665.
Eley C (2016). Design Professional’s Guide to Zero Net Energy
Buildings. Washington DC: Island Press.
Electric Power Research Institute (2018). Load Shape Library. Available
at Accessed 21 Jan 2018.
Elliott DB, Anderson DM, Belzer DB, Dirks JA (2004). Methodological
framework for analysis of buildings-related programs. The GPRA
metrics effort. Richland, WA, USA: Pacific Northwest National Lab.
Emmanuel W, Jérôme K (2015). A verification of CitySim results
using the BESTEST and monitored consumption values. In:
Proceedings of Building Simulation Applications Conference,
Bolzano, Italy.
Energy and Environmental Economics (E3) (2016). Introduction—
EnergyPATHWAYS Documentation. Available at http:// Accessed
21 Jan 2018.
ENERGY STAR (2017). Unit Shipment Data. Available at https://
ENERGY STAR (2018). ENERGY STAR Certified Products. Available
at Accessed 15 Aug 2017.
EPA Office of Air and Radiation (2017). National Awareness of
ENERGY STAR for 2016: Analysis of 2016 CEE Household Survey.
Washington DC: U.S. Environmental Protection Agency.
European Commission – Energy (2018). EU Building Stock Observatory.
Available at Accessed
21 January 2018.
Fabi V, Andersen RV, Corgnati S, Olesen BW (2012). Occupants’
window opening behaviour: A literature review of factors influencing
occupant behaviour and models. Building and Environment, 58:
Fang Z, Li N, Li B, Luo G, Huang Y (2014). The effect of building
envelope insulation on cooling energy consumption in summer.
Energy and Buildings, 77: 197–205.
Farese P, Gelman R, Hendron R (2012). A Tool to Prioritize Energy
Efficiency Investments. National Renewable Energy Laboratory.
Available at
Frei B, Sagerschnig C, Gyalistras D (2017). Performance gaps in Swiss
buildings: An analysis of conflicting objectives and mitigation
strategies. Energy Procedia, 122: 421–426.
Fuchs M, Teichmann J, Lauster M, Streblow R, Müller D (2015).
Approach for simulation-based scenario analyses of district energy
systems. In: Proceedings of the 28th International Conference on
Efficiency, Cost, Optimization, Simulation and Environmental
Impact of Energy Systems.
Gaetani I, Hoes PJ, Hensen JLM (2016). Occupant behavior in
building energy simulation: Towards a fit-for-purpose modeling
strategy. Energy and Buildings, 121: 188–204.
Galvin R (2014). Making the ‘rebound effect’ more useful for
performance evaluation of thermal retrofits of existing homes:
Defining the ‘energy savings deficit’ and the ‘energy performance
gap.’ Energy and Buildings, 69: 515–524.
Garrett A, New J (2015). Scalable tuning of building models to hourly
data. Energy, 84: 493–502.
Gao D, Wang S, Shan K, Yan C (2016). A system-level fault detection
and diagnosis method for low delta-T syndrome in the complex
HVAC systems. Applied Energy, 164: 1028–1038.
Ghahramani A, Jazizadeh F, Becerik-Gerber B (2014). A knowledge
based approach for selecting energy-aware and comfort-driven
HVAC temperature set points. Energy and Buildings, 85: 536–548.
Gilshannon ST, Brown DR (1996). Review of methods for forecasting
the market penetration of new technologies. U.S. Department of
Energy, Pacific Northwest Laboratory, PNNL-11428. Available at
Göçer Ö, Hua Y, Göçer K (2015). Completing the missing link in
building design process: Enhancing post-occupancy evaluation
method for effective feedback for building performance. Building
and Environment, 89: 14–27.
Hong et al. / Building Simulation
Google Labs (2011). Google Correlate. Available at https:// Accessed 17 Aug 2017.
Google Labs (2018). Google Trends. Available at
trends. Accessed 17 Aug 2017.
Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF
(2010). The ODD protocol: A review and first update. Ecological
Modelling, 221: 2760–2768.
Gunay HB, O’Brien W, Beausoleil-Morrison I (2013). A critical review
of observation studies, modeling, and simulation of adaptive
occupant behaviors in offices. Building and Environment, 70:
Haldi F, Robinson D (2009). Interactions with window openings by
office occupants. Building and Environment, 44: 2378–2395.
Haldi F, Robinson D (2010). Adaptive actions on shading devices in
response to local visual stimuli. Journal of Building Performance
Simulation, 3: 135–153.
Han H, Cao Z, Gu B, Ren N (2012). PCA-SVM-Based Automated
Fault Detection and Diagnosis (AFDD) for vapor-compression
refrigeration systems. HVAC&R Research, 16: 295–313.
Harish VSKV, Kumar A (2016). A review on modeling and simulation
of building energy systems. Renewable and Sustainable Energy
Reviews, 56: 1272–1292.
Haas R, Biermayr P (2000). The rebound effect for space heating
empirical evidence from Austria. Energy Policy, 28: 403–410.
Hens H, Parijs W, Deurinck M (2010). Energy consumption for
heating and rebound effects. Energy and Buildings, 42: 105–110.
Hensen JLM, Lamberts R (2011). Building Performance Simulation
for Design and Operation. Abingdon, UK: Spon Press.
Henser DA, Rose JM, Greene WH (2015). Applied Choice Analysis: A
Primer, 2nd edn. Cambridge, UK: Cambridge University Press.
Heo Y, Choudhary R, Augenbroe GA (2012). Calibration of building
energy models for retrofit analysis under uncertainty. Energy and
Buildings, 47: 550–560.
Heschong Mahone Group (2012). Road to ZNE: Mapping
Pathways to ZNE Buildings in California. Available at https://
inal-report_withappendices.pdf. Accessed 28 Jan 2018.
Heydarian A, Becerik-Gerber B (2017). Use of immersive virtual
environments for occupant behaviour monitoring and data
collection. Journal of Building Performance Simulation, 10: 484–498.
Higgins A, Paevere P, Gardner J, Quezada G (2012). Combining
choice modelling and multi-criteria analysis for technology
diffusion: An application to the uptake of electric vehicles.
Technological Forecasting and Social Change, 79: 1399–1412.
Higgins A, Syme M, McGregor J, Marquez L, Seo S (2014). Forecasting
uptake of retrofit packages in office building stock under
government incentives. Energy Policy, 65: 501–511.
Hong T, Zhang J, Jiang Y (1997). IISABRE: An integrated building
simulation environment. Building and Environment, 32: 219–224.
Hong T, Chou SK, Bong TY (2000). Building simulation: An overview of
developments and information sources. Building and Environment,
35: 347–361.
Hong T, Fisk WJ (2010). Assessment of energy savings potential from
the use of demand controlled ventilation in general office spaces
in California. Building Simulation, 3: 117–124.
Hong T, Lin HW (2013). Occupant behavior: Impact on energy use
of private offices. In: Proceedings of the 1st Asia Conference
of International Building Performance Simulation Association
(ASim 2012), Shanghai, China.
Hong T, Chang W-K, Lin H-W (2013). A fresh look at weather impact
on peak electricity demand and energy use of buildings using
30-year actual weather data. Applied Energy, 111: 333–350.
Hong T, Piette MA, Chen Y, Lee SH, Taylor-Lange SC, Zhang R, Sun K,
Price P (2015a). Commercial building energy saver: An energy
retrofit analysis toolkit. Applied Energy, 159: 298–309.
Hong T, D’Oca S, Taylor-Lange SC, Turner WJN, Chen Y, Corgnati SP
(2015b). An ontology to represent energy-related occupant behavior
in buildings. Part II: Implementation of the DNAS framework
using an XML schema. Building and Environment, 94: 196–205.
Hong T, DOca S, Turner W, Taylor-Lange SC (2015c). An ontology
to represent energy-related occupant behavior in buildings. Part I:
Introduction to the DNAs framework. Building and Environment,
92: 764–777.
Hong T, Chen Y, Lee SH, Piette MA (2016a). CityBES: A web-based
platform to support city-scale building energy efficiency. In:
Proceedings of the 5th International Urban Computing Workshop,
San Francisco, USA.
Hong T, Sun K, Zhang R, Hinokuma R, Kasahara S, Yura Y (2016b).
Development and validation of a new variable refrigerant flow
system model in EnergyPlus. Energy and Buildings, 117: 399–411.
Hong T, Sun H, Chen Y, Taylor-Lange SC, Yan D (2016c). An
occupant behavior modeling tool for co-simulation. Energy and
Buildings, 117: 272–281.
Hong T, Yan D, D’Oca S, Chen CF (2017a). Ten questions concerning
occupant behavior in buildings: The big picture. Building and
Environment, 114: 518–530.
Hong T, Kim J, Jeong J, Lee M, Ji C (2017b). Automatic calibration
model of a building energy simulation using optimization
algorithm. Energy Procedia, 105: 3698–3704.
Hong T (2018). IEA EBC annexes advance technologies and strategies
to reduce energy use and GHG emissions in buildings and
communities. Energy and Buildings, 158: 147–149.
Institute for Building Efficiency (2016). 2016 Energy Efficiency Indicator
Survey. Milwaukee, WI, USA: Institute for Building Efficiency.
International Energy Agency. (2017). Energy Technology Perspectives
2017: Catalysing Energy Technology Transformations. Available
International Energy Agency (IEA) Energy in Buildings and Communities
Programme (2018). IEA-EBC Annex 70—Building Energy
Epidemiology: Analysis of Real Building Energy Use at Scale.
Available at Accessed 21 Sept
Jaccard M, Dennis M (2006). Estimating home energy decision
parameters for a hybrid energy-economy policy model.
Environmental Modeling and Assessment, 11: 91–100.
Johnson NR (2017). Building Energy Model Calibration for Retrofit
Decision Making. Master Thesis, Portland State University, USA.
Joint Global Change Research Institute (2017). Global Change
Assessment Model. Available at
gcam. Accessed 21 Jan 2018.
Hong et al. / Building Simulation
Jones RV, Fuertes A, de Wilde P (2015). The gap between simulated
and measured energy performance: A case study across six identical
new-build flats in the UK. In: Proceedings of International
Conference of the International Building Performance Simulation
Karaguzel OT, Zhang R, Lam KP (2014). Coupling of whole-building
energy simulation and multi-dimensional numerical optimization
for minimizing the life cycle costs of office buildings, Building
Simulation, 7: 111–121.
Karlsson F, Rohdin P, Persson ML (2007). Measured and predicted
energy demand of a low energy building: Important aspects when
using Building Energy Simulation. Building Services Engineering
Research and Technology, 28: 223–235.
Keirstead J, Jennings M, Sivakumar A (2012). A review of urban
energy system models: Approaches, challenges and opportunities.
Renewable and Sustainable Energy Reviews, 16: 3847–3866.
Kim YJ, Park CS (2016). Stepwise deterministic and stochastic calibration
of an energy simulation model for an existing building. Energy
and Buildings, 133: 455–468.
Kim YS, Heidarinejad M, Dahlhausen M, Srebric J (2017). Building
energy model calibration with schedules derived from electricity
use data. Applied Energy, 190: 997–1007.
Kok N, McGraw M, Quigley JM (2012). The diffusion over time and
space of energy efficiency in building. Annals of Regional Science,
48: 541–564.
Koomey J (2000). Avoiding “the big mistake” in forecasting technology
adoption. In: Proceedings of the Energex Conference.
Kristensen MH, Choudhary R, Petersen S (2017). Bayesian calibration
of building energy models: Comparison of predictive accuracy
using metered utility data of different temporal resolution. Energy
Procedia, 122: 277–282.
La Fleur L, Moshfegh B, Rohdin P (2017). Measured and predicted
energy use and indoor climate before and after a major renovation
of an apartment building in Sweden. Energy and Buildings, 146:
Lam KP, Zhao J, Ydstie EB, Wirick J, Qi M, Park J (2014). An
EnergyPlus whole building energy model calibration method for
office buildings using occupant behavior data mining and empirical
data. In: Proceedings of 2014 ASHRAE/IBPSA-USA Building
Simulation Conference, pp. 160–167.
Langevin J, Gurian PL, Wen J (2015a). Tracking the human-building
interaction: A longitudinal field study of occupant behavior in
air-conditioned offices. Journal of Environmental Psychology, 42:
Langevin J, Wen J, Gurian PL (2015b). Simulating the human-building
interaction: Development and validation of an agent-based
model of office occupant behaviors. Building and Environment,
88: 27–45.
Langevin J, Wen J, Gurian PL (2016). Quantifying the human-building
interaction: Considering the active, adaptive occupant in building
performance simulation. Energy and Buildings, 117: 372–386.
Lara RA, Naboni E, Pernigotto G, Cappelletti F, Zhang Y, Barzon F,
Romagnoni P (2017). Optimization tools for building energy
model calibration. Energy Procedia, 111: 1060–1069.
Lauro F, Moretti F, Capozzoli A, Khan I, Pizzuti S, Macas M, Panzieri
S (2014). Building fan coil electric consumption analysis with fuzzy
approaches for fault detection and diagnosis. Energy Procedia,
62: 411–420.
Lawrence Berkeley National Laboratory (2018). FLEXLAB: Facility
for Low-Energy eXperiments in Buildings. Available at https:// Accessed 14 Jan 2018.
Lee YS, Malkawi AM (2014). Simulating multiple occupant behaviors
in buildings: An agent-based modeling approach. Energy and
Buildings, 69: 407–416.
Lee T, Yao R, Coker P (2014). An analysis of UK policies for domestic
energy reduction using an agent based tool. Energy Policy, 66:
Lee SH, Hong T, Piette MA, Sawaya G, Chen Y, Taylor-Lange SC
(2015). Accelerating the energy retrofit of commercial buildings
using a database of energy efficiency performance. Energy, 90:
Lee SH, Hong T (2017). Leveraging zone air temperature data to
improve physics-based energy simulation of existing buildings.
In: Proceedings of International IBPSA Building Simulation
Conference, San Francisco, USA.
Lee S, Bilionis I, Karava P, Tzempelikos A (2017). A Bayesian approach
for probabilistic classification and inference of occupant thermal
preferences in office buildings. Building and Environment, 118:
Li J (2011). Modeling household energy consumption and adoption
of energy-efficient technology using recent micro-data. In:
Proceedings of USAEE/IAEE North American Conference,
Redefining the Energy Economy: Changing Roles of Industry,
Government, and Research, Washington DC, USA.
Li X, Wen J (2014). Review of building energy modeling for control
and operation. Renewable and Sustainable Energy Reviews, 37:
Li C, Hong T, Yan D (2014). An insight into actual energy use and
its drivers in high-performance buildings. Applied Energy, 131:
Li D, Hu G, Spanos CJ (2016a). A data-driven strategy for detection
and diagnosis of building chiller faults using linear discriminant
analysis. Energy and Buildings, 128: 519–529.
Li Q, Augenbroe G, Brown J (2016b). Assessment of linear emulators
in lightweight Bayesian calibration of dynamic building energy
models for parameter estimation and performance prediction.
Energy and Buildings, 124: 194–202.
Liang X, Hong T, Shen GQ (2016). Improving the accuracy of energy
baseline models for commercial buildings with occupancy data.
Applied Energy, 179: 247–260.
Lim H, Zhai ZJ (2017a). Comprehensive evaluation of the influence
of meta-models on Bayesian calibration. Energy and Buildings,
155: 66–75.
Lim H, Zhai ZJ (2017b). Review on stochastic modeling methods for
building stock energy prediction. Building Simulation, 10: 607–624.
Lin H-W, Hong T (2013). On variations of space-heating energy use
in office buildings. Applied Energy, 111: 515–528.
Luo X, Hong T, Chen Y, Piette MA (2017). Electric load shape
benchmarking for small- and medium-sized commercial buildings.
Applied Energy, 204: 715–725.
Hong et al. / Building Simulation
Maile T, Bazjanac V, Fischer M (2012). A method to compare simulated
and measured data to assess building energy performance. Building
and Environment, 56: 241–251.
Manfren M, Aste N, Moshksar R (2013). Calibration and uncertainty
analysis for computer models—A meta-model based approach
for integrated building energy simulation. Applied Energy, 103:
Marmaras JM (2014). Measurement and verification—Retro-
commissioning of a LEED gold rated building through means
of an energy model: Are aggressive energy simulation models
reliable? Master Thesis, University of Massachusetts Amherst,
Menezes AC, Cripps A, Bouchlaghem D, Buswell R (2012). Predicted
vs. actual energy performance of non-domestic buildings: Using
post-occupancy evaluation data to reduce the performance gap.
Applied Energy, 97: 355–364.
Mihai A, Zmeureanu R (2017). Bottom-up evidence-based calibration
of the HVAC air-side loop of a building energy model. Journal of
Building Performance Simulation, 10: 105–123.
Mills E, Bourassa N, Piette MA (2005). The cost-effectiveness of
commissioning new and existing commercial buildings: Lessons
from 224 buildings. In: Proceedings of the National Conference
on Building Commissioning.
MIT Sustainable Design Group (2016). Boston Citywide Energy Model.
Available at
BostonEnergyModel. Accessed 15 May 2016.
Mirakhorli A, Dong B (2016). Occupancy behavior based model
predictive control for building indoor climate—A critical review.
Energy and Buildings, 129: 499–513.
Moglia M, Cook S, McGregor J (2017). A review of Agent-Based
Modelling of technology diffusion with special reference to residential
energy efficiency. Sustainable Cities and Society, 31: 173–182.
Morari M, Lee JH (1999). Model predictive control: Past, present and
future. Computers & Chemical Engineering, 23: 667–682.
Muehleisen RT, Bergerson J, Collier N, Graziano DJ, Tatara E (2016).
Agent based technology adoption model for program planning
and design. In: Proceedings of 2016 ACEEE Summer Study on
Energy Efficiency in Buildings.
Müller MO (2013). Diffusion Dynamics of Energy-Efficient Renovations:
Causalities and Policy Recommendations. Berlin: Springer.
Munir S, Stankovic JA, Liang CJM, Lin S (2013). Cyber physical system
challenges for human-in-the-loop control. In: Proceedings of the
8th International Workshop on Feedback Computing.
Nachtrieb R (2013). System Dynamics model of residential and com-
mercial lighting markets. In: Proceedings of the 31 International
Conference of the System Dynamics Society.
Najafi M, Auslander DM, Bartlett PL, Haves P, Sohn MD (2012).
Application of machine learning in the fault diagnostics of air
handling units. Applied Energy, 96: 347–358.
Nagy Z, Yong FY, Frei M, Schlueter A (2015). Occupant centered
lighting control for comfort and energy efficient building operation.
Energy and Buildings, 94: 100–108.
National Center for Atmospheric Research (2018). Weather Research
and Forecasting Model. Available at
weather-research-and-forecasting-model. Accessed 14 Jan 2018.
National Electrical Manufacturers Association (NEMA) (2018). Lamp
Indices. Available at
Lamp-Indices.aspx. Accessed 11 Sept 2017.
National Renewable Energy Laboratory. (2017). ResStock Analysis
Tool. Available at
Accessed 21 Jan 2018.
National Renewable Energy Laboratory (2018). UrbanOpt. Available
at Accessed 8 Jan
Navigant Consulting (2013). Demand-Side Resource Potential. Study
Report. Prepared for: Kansas City Power and Light Energizing
Navigant Consulting (2016). Updated Buildings Sector Appliance and
Equipment Costs and Efficiency. Available at
Navigant Consulting (2017). Residential End Uses: Historical Efficiency
Data and Incremental Installed Costs for Efficiency Upgrades.
Navigant Consulting and Lawrence Berkeley National Laboratory (2011).
Technical Support Document: Energy Efficiency Program for
Consumer Products and Commercial and Industrial Equipment:
Residential Clothes Dryers and Room Air Conditioners.
Navigant Consulting and Lawrence Berkeley National Laboratory (2014).
Technical Support Document: Energy Efficiency Program for
Consumer Products and Commercial and Industrial Equipment:
General Service Lamps. Available at
Navigant Consulting and Pacific Northwest National Laboratory (2014).
Technical Support Document: Energy Efficiency Program
for Consumer Products and Commercial and Industrial
Equipment: Commercial Packaged Boilers. Available at https://!documentDetail;D=EERE-2010-BT-STD-
Navigant Consulting and Pacific Northwest National Laboratory (2016).
Technical Support Document: Energy Efficiency Program for
Consumer Products and Commercial and Industrial Equipment:
Commercial Water Heating Equipment.
Nemtzow D (2017). Buildings and the Grid 101: Why Does it Matter
for Energy Efficiency? Available at
energy-efficiency. Accessed 21 Jan 2018.
New Buildings Institute (2018). Getting to Zero Database. Available at Accessed
28 Jan 2018.
Noonan DS, Hsieh LHC, Matisoff D (2013). Spatial effects in energy-
efficient residential HVAC technology adoption. Environment
and Behavior, 45: 476–503.
Østergård T, Jensen RL, Maagaard SE (2016). Building simulations
supporting decision making in early design—A review. Renewable
and Sustainable Energy Reviews, 61: 187–201.
Otter M, Elmqvist H, Blochwitz T, Mauss J, Junghanns A, Olsson
H (2011). Functional Mockup Interface—Overview. Available at
Palmer KA, Hale WT, Such KD, Shea BR, Bollas GM (2016). Optimal
design of tests for heat exchanger fouling identification. Applied
Thermal Engineering, 95: 382–393.
Hong et al. / Building Simulation
Packey DJ (1993). Market penetration of new energy technologies.
NREL/TP-462-4860. Golden, CO, USA: National Renewable
Energy Laboratory.
Pang X, Wetter M, Bhattacharya P, Haves P (2012). A framework for
simulation-based real-time whole building performance assessment.
Building and Environment, 54: 100–108.
Pang X, Nouidui TS, Wetter M, Fuller D, Liao A, Haves P (2016).
Building energy simulation in real time through an open standard
interface. Energy and Buildings, 117: 282–289.
Papadopoulos S, Azar E (2016). Integrating building performance
simulation in agent-based modeling using regression surrogate
models: A novel human-in-the-loop energy modeling approach.
Energy and Buildings, 128: 214–223.
Piette MA, Kinney SK, Haves P (2001). Analysis of an information
monitoring and diagnostic system to improve building operations.
Energy and Buildings, 33: 783–791.
Piette MA, Hong T, Wetter M, et al. (2016). Hierarchical occupancy
responsive model predictive control at room, building and
campus levels. A proposal of the joint U.S.-China CERC-BEE
Pomponi F, Moncaster A (2018). Scrutinising embodied carbon in
buildings: The next performance gap made manifest. Renewable
and Sustainable Energy Reviews, 81: 2431–2442.
Raju PS, Teotia APS (1985). An Evaluation of Market Penetration
Forecasting Methodologies for New Residential and Commercial
Energy Technologies. U.S. Department of Energy, Office of Minority
Economic Impact Research and Education Division.
Radhakrishnan N, Su Y, Su R, Poolla K (2016). Token based scheduling
for energy management in building HVAC systems. Applied
Energy, 173: 67–79.
Ramos Ruiz G, Fernández Bandera C, Gómez-Acebo Temes T,
Sánchez-Ostiz Gutierrez A (2016). Genetic algorithm for building
envelope calibration. Applied Energy, 168: 691–705.
Ratcliffe C, Chen H, Shanks TRW, Nam Y, Schreiner M, Zhan M,
Sherraden M (2007). Assessing Asset Data on Low-Income
Households: Current Availability and Options for Improvement.
Available at
Reddy TA (2006). Literature Review on Calibration of Building Energy
Simulation Programs. ASHRAE Transactions, 112: 226–240.
Regnier C, Sun K, Hong T, Piette MA (2018). Quantifying the benefits
of a building retrofit using an integrated system approach: A case
study. Energy and Buildings, 159: 332–345.
Reinhart CF, Dogan T, Jakubiec JA, Rakha T, Sang A (2013).
Umi—An urban simulation environment for building energy use,
daylighting and walkability. In: Proceedings of the 13th International
IBPSA Building Simulation Conference, Chambéry, France.
Reinhart CF, Davila CC (2016). Urban building energy modeling—A
review of a nascent Field. Building and Environment, 97:196–202.
Rocky Mountain Institute (2010). Black Belt Energy Modeling.
Available at
x_2010.pdf. Accessed 28 Jan 2018.
Robinson JF, Foxon TJ, Taylor PG (2016). Performance gap analysis
case study of a non-domestic building. Proceedings of the Institution
of Civil Engineers—Engineering Sustainability, 169: 31–38.
Rogers EM (1995). Diffusion of Innovations. Available at https://
Roth A (2017). Building Energy Modeling 101: What Is It and What
Is DOE’s Role? Available at
role. Accessed 28 Jan 2018.
Salakij S, Yu N, Paolucci S, Antsaklis P (2016). Model-based predictive
control for building energy management 1: Energy modeling and
optimal control. Energy and Buildings, 133: 345–358.
Samuelson HW, Ghorayshi A, Reinhart CF (2016). Analysis of a
simplified calibration procedure for 18 design-phase building
energy models. Journal of Building Performance Simulation, 9:
Shen E, Hong T (2009). Simulation-based assessment of the energy
savings benefits of integrated control in office buildings. Building
Simulation, 2: 239–351.
Sokol J, Cerezo Davila C, Reinhart C (2017). Validation of a Bayesian-
based method for defining residential archetypes in urban building
energy models. Energy and Buildings, 134: 11–24.
Sopha BM, Klöckner CA, Hertwich EG (2013). Adoption and
diffusion of heating systems in Norway: Coupling agent-based
modeling with empirical research. Environmental Innovation
and Societal Transitions, 8: 42–61.
Sorrell S, Dimitropoulos J, Sommerville M (2009). Empirical estimates
of the direct rebound effect: A review. Energy Policy, 37: 1356–
Sun Y (2014). Closing the building energy performance gap by improving
our predictions. PhD Thesis, Georgia Tech, USA.
Sun K, Yan D, Hong T, Guo S (2014). Stochastic modeling of overtime
occupancy and its application in building energy simulation and
calibration. Building and Environment, 79: 1–12.
Sun K, Hong T, Taylor-Lange SC, Piette MA (2016). A pattern-based
automated approach to building energy model calibration. Applied
Energy, 165: 214–224.
Sun K, Hong T (2017a). A framework for quantifying the impact of
occupant behavior on energy savings of energy conservation
measures. Energy and Buildings, 146: 383–396.
Sun K, Hong T (2017b). A simulation approach to estimate energy
savings potential of occupant behavior measures. Energy and
Buildings, 136: 43–62.
Sunikka-Blank M, Galvin R (2012). Introducing the prebound effect:
The gap between performance and actual energy consumption.
Building Research and Information, 40: 260–273.
Tofield B (2012). Delivering a low-energy low energy building: Making
quality commonplace. Build with CaRe report, University of East
Anglia, Norwich.
Transportation Utility Management System (2018). Available at
rsrces/util/tums.aspx. Accessed 14 Jan 2018.
Trčka M, Hensen JLM, Wetter M (2009). Co-simulation of innovative
integrated HVAC systems in buildings. Journal of Building
Performance Simulation, 2: 209–230.
Hong et al. / Building Simulation
Turner C, Frankel M (2008). Energy Performance of LEED® for New
Construction Buildings. New Buildings Institute.
U.S. Census Bureau (2017a). American Community Survey (ACS).
Available at
Accessed 21 Aug 2017.
U.S. Census Bureau (2017b). American Housing Survey (AHS).
Available at
Accessed 21 Aug 2017.
U.S. Department of Commerce. (2017). Data Archive. Available at Accessed 17 Aug 2017.
U.S. Department of Energy Advanced Research Projects Agency
(2014). DELTA Program Overview.
U.S. Department of Energy (2015a). A common definition of zero
energy buildings. Available at
093015.pdf. Accessed 28 Jan 2018.
U.S. Department of Energy (2015b). Wireless Occupancy Sensors for
Lighting Controls: An Applications Guide for Federal Facility
U.S. Department of Energy (2018a). EnergyPlus. Available at Accessed 14 Jan 2018.
U.S. Department of Energy (2018b). Sensors and Controls R&D.
Available at
controls-rd-0. Accessed 21 Jan 2018.
U.S. Department of Energy (2018c). Exascale Computing Project.
Available at Accessed 14 Jan
U.S. Department of Energy (2018d). Building Performance Database.
Available at
database. Accessed 21 Jan 2018.
U.S. Department of Energy (2018e). Building Energy Data Exchange
Specification (BEDES). Available at
Accessed 21 Jan 2018.
U.S. Department of Energy Appliance Standards Program (2016).
Commercial Packaged Boiler National Impact Analysis (NIA).
Available at
2013-BT-STD-0030-0047. Accessed 15 Aug 2017.
U.S. Department of Energy Building Technologies Office (2016).
Multi-year program plan. Available at
files/2016/02/f29/BTO_MYPP_2016.pdf. Accessed 28 Jan 2018.
U.S. Department of Energy Building Technologies Office (BTO)
(2017). Scout. Available at
scout. Accessed 17 Aug 2017.
U.S. Energy Information Administration (1998). Impacts of the
Kyoto Protocol on US Energy Markets and Economic Activity.
Energy Information Administration Report. Available at http://
U.S. Energy Information Administration (2015). An Assessment of
Interval Data and Their Potential Application to Residential
Electricity End-Use Modeling.
U.S. Energy Information Administration (2016). Commercial Buildings
Energy Consumption Survey (CBECS). Available at https:// Accessed 15 Aug 2017.
U.S. Energy Information Administration (2017a). Annual Energy
Outlook 2017. Available at
Accessed 15 Aug 2017.
U.S. Energy Information Administration (2017b). Commercial
Demand Module of the National Energy Modeling System:
Model Documentation.
U.S. Energy Information Administration (2017c). Residential Demand
Module of the National Energy Modeling System: Model
U.S. Energy Information Administration (2017d). Residential Energy
Consumption Survey (RECS). Available at
consumption/residential. Accessed 15 Aug 2017.
Van Dronkelaar C, Dowson M, Spataru C, Mumovic D (2016). A
review of the energy performance gap and its underlying causes
in non-domestic buildings. Frontiers in Mechanical Engineering,
Wagner A, O’Brien L, Dong B (2018). Exploring Occupant Behavior
in Buildings: Methods and Challenges. Cham, Switzerland:
Springer International.
Wang S, Cui J (2005). Sensor-fault detection, diagnosis and estimation
for centrifugal chiller systems using principal-component analysis
method. Applied Energy, 82: 197–213.
Wang C, Yan D, Jiang Y (2011). A novel approach for building
occupancy simulation. Building Simulation, 4: 149–167.
Wang L, Greenberg S, Fiegel J, Rubalcava A, Earni S, Pang X, Yin R,
Woodworth S, Hernandez-Maldonado J (2013). Monitoring-based
HVAC commissioning of an existing office building for energy
efficiency. Applied Energy, 102: 1382–1390.
Wang H, Zhai Z (2016). Advances in building simulation and com-
putational techniques: A review between 1987 and 2014. Energy
and Buildings, 128: 319–335.
Weiss M, Junginger M, Patel MK, Blok K (2010). A review of experience
curve analyses for energy demand technologies. Technological
Forecasting and Social Change, 77: 411–428.
Wetter M (2009). Modelica-based modelling and simulation to support
research and development in building energy and control systems.
Journal of Building Performance Simulation, 2: 143–161.
Wetter M (2011). Co-simulation of building energy and control
systems with the Building Controls Virtual Test Bed. Journal of
Building Performance Simulation, 4: 185–203.
Wetter M, Fuchs M, Grozman M, Helsen P, Jorissen L, Müller F,
Nytsch-Geusen D, Picard C, Sahlin D, Thorade P (2015a). IEA
EBC Annex 60 Modelica Library—An international collaboration
to develop a free open-source model library for buildings
and community energy systems. In: Proceedings of the 14th
International IBPSA Building Simulation Conference, Hyderabad,
Wetter M, Bonvini M, Nouidui TS (2015b). Equation-based
languages—A new paradigm for building energy modeling,
simulation and optimization. Energy and Buildings, 117: 290–300.
Wilkerson JT, Cullenward D, Davidian D, Weyant JP (2013). End use
technology choice in the National Energy Modeling System
(NEMS): An analysis of the residential and commercial building
sectors. Energy Economics, 40: 773–784.
Hong et al. / Building Simulation
Williams A, Atkinson B, Garbesi K, Page E, Rubinstein F (2012a). Lighting
controls in commercial buildings. LEUKOS, 8(3): 161–180.
Williams JH, DeBenedictis A, Ghanadan R, Mahone A, Moore J, et al.
(2012b). The technology path to deep greenhouse gas emissions
cuts by 2050: The pivotal role of electricity. Science, 335(6064):
Yan D, O’Brien W, Hong T, Feng X, Burak Gunay H, Tahmasebi F,
Mahdavi A (2015). Occupant behavior modeling for building
performance simulation: Current state and future challenges.
Energy and Buildings, 107: 264–278.
Yan D, Hong T, Dong B, Mahdavi A, D’Oca S, Gaetani I, Feng X
(2017). IEA EBC Annex 66: Definition and simulation of occupant
behavior in buildings. Energy and Buildings, 156: 258–270.
Yin R, Kiliccote S, Piette MA (2014). Linking measurements and
models in commercial buildings: A case study for model calibration
and demand response strategy evaluation. Energy and Buildings,
124: 222–235.
Yoshino H, Hong T, Nord N (2017). IEA EBC annex 53: Total energy
use in buildings—Analysis and evaluation methods. Energy and
Buildings, 152, 124–136.
Yun GY, Song K (2017). Development of an automatic calibration
method of a VRF energy model for the design of energy efficient
buildings. Energy and Buildings, 135: 156–165.
Zero Carbon Hub (2014). Closing the Gap Between Design & As-built
Performance. Available at
Zhang T, Nuttall WJ (2007). An agent based simulation of smart
metering technology adoption. International Journal of Agent
Technologies and Systems (IJATS). Available at
Zhang R, Hong T (2017). Modeling of HVAC operational faults in
building performance simulation. Applied Energy, 202: 178–188.
Zhao Y, Wang S, Xiao F (2013). Pattern recognition-based chillers
fault detection method using Support Vector Data Description
(SVDD). Applied Energy, 112: 1041–1048.
Zhou X, Hong T, Yan D (2014). Comparison of HVAC system modeling
in EnergyPlus, DeST and DOE-2.1E. Building Simulation, 7:
Zhu D, Hong T, Yan D, Wang C (2013). A detailed loads comparison
of three building energy modeling programs: EnergyPlus, DeST
and DOE-2.1E. Building Simulation, 6: 323–335.
Zibin N, Zmeureanu R, Love J (2016). Automatic assisted calibration
tool for coupling building automation system trend data with
commissioning. Automation in Construction, 61: 124–133.
... Quantifying urban-scale energy use in buildings is a complex, cross-sectoral, and multi-scale task, with the entire process highly dependent on the urban environment [5]. Hong et al. (2018) mention that the emergent of building energy modelling is one of the optimum supports available for energy efficiency, resilience, and sustainability [6], and the model extensively improves energy performance rating, energy-efficient design, code compliance, and optimised operations. Reinhart and Cerezo Davila (2016) explain that the urban building energy model could accommodate many spatial scale sizes from a block, district, and, ultimately, an entire city [7]. ...
... Quantifying urban-scale energy use in buildings is a complex, cross-sectoral, and multi-scale task, with the entire process highly dependent on the urban environment [5]. Hong et al. (2018) mention that the emergent of building energy modelling is one of the optimum supports available for energy efficiency, resilience, and sustainability [6], and the model extensively improves energy performance rating, energy-efficient design, code compliance, and optimised operations. Reinhart and Cerezo Davila (2016) explain that the urban building energy model could accommodate many spatial scale sizes from a block, district, and, ultimately, an entire city [7]. ...
Full-text available
Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.
... Building load forecasting is an emerging area of building performance simulation (BPS), facing technical complexity and major significance to a variety of stakeholders, since it supports future operational and energy efficiency improvements in existing buildings [128]. Deep learning models have entered the load forecasting field in recent years due to their ability to deal with big data and lead to high forecasting accuracies. ...
Full-text available
Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.
... Building performance simulation (BPS) provides effective results in quantifying the effect of planned refurbishment actions in existing buildings and in predicting the new buildings' performances before they are built (Hong, Langevin, & Sun, 2018). In order to facilitate the decision-making process, exploration, and evaluation of design alternatives, BPS is frequently used in the construction sector and academia (Crawley, Hand, Kummert, & Griffith, 2008). ...
Existing buildings are seen as a potential field where effective carbon-saving and energy-efficiency strategies can be applied. Their lifetime and performance can be increased by implementing different retrofit measures. The retrofit processes remain a complex task that necessitates long-term planning and costly operations and requires a consensus of their stakeholders to identify an ultimate solution from many alternatives. This paper demonstrates a simulation-based exploratory approach, allowing a broader consideration of alternative retrofit solutions to increase non-technical and technical stakeholders’ involvement over design decisions. A hypothetical office building was selected, and a list of retrofit parameters and their combinations focusing on multiple objectives was determined, modeled, simulated, and visualized. Three different decision-making approaches are presented and discussed over the case study results. The study suggests that giving a voice to all stakeholders in decision-making processes might lead to achieving more robust retrofit solutions.
... Complementary to the reviews of general OB modeling, the OB in the context of building simulation and in the context of building control has also been the focus of several recent studies [18][19][20][21][22]. ...
The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. The literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.
... The digital representation of the building point clouds enables and facilitates new applications in a variety of subsequent tasks, including simulation (Hong et al., 2018), smart city (Ruohomäki et al., 2018), monitoring (Begić et al., 2021), reconstruction (Previtali et al., 2018), and geographic information systems (GIS) update (Dukai et al., 2020). In these applications, the automatic classification of high Level-of-Detail (LoD) buildings is a fundamental and critical task. ...
Full-text available
As a result of the success of Deep Learning (DL) techniques, DL-based approaches for extracting information from 3D building point clouds have evolved in recent years. Despite noteworthy progress in existing methods for interpreting point clouds, the excessive cost of annotating 3D data has resulted in DL-based 3D point cloud understanding tasks still lagging those for 2D images. The notion that pre-training a network on a large source dataset may help enhance performance after it is fine-tuned on the target task and dataset has proved vital in numerous tasks in the Natural Language Processing (NLP) domain. This paper proposes a straightforward but effective pre-training method for 3D building point clouds that learns from a large source dataset. Specifically, it first learns the ability of semantic segmentation by pre-training on a cross-domain source Stanford 3D Indoor Scene Dataset. It then initialises the downstream networks with the pre-trained weights. Finally, the models are fine-tuned with the target building scenes obtained from the ArCH benchmarking dataset. Our paper evaluates the proposed method by employing four fully supervised networks as backbones. The results of two pipelines are compared between training from scratch and pre-training. The results illustrate that pre-training on the source dataset can consistently improve the performance of the target dataset with an average gain of 3.9%.
... Still, there are also other reasons for showing different energy-saving profiles, such as typical occupant behaviours being highly stochastic [27] including their random perceptions of the space [64,65,86]. Interior space allocation/arrangement [27,64,65] and indoor ambient data [18,35,87] also play an essential role in changing the energy consumption pattern. In addition, in all cases the energy medians were uneven, and the interquartile ranges varied due to the occupants' stochastic energy consumption behaviour. ...
Interior layouts of a building may influence the presence and movement of occupants, which can lead them to participate in a certain activity, energy-saving behaviour for instance, which occurs at a particular location within an indoor space. Moreover, rearranging this interior layout can help understanding how and why occupants use more energy and encourage their energy-saving behaviours through occupancy-based interventions. However, only a handful of studies have attempted to evaluate the effects of interior layout on the energy-saving behaviour of occupants. In light of this, this study offers a comprehensive modelling framework for investigating the influence of interior layouts on occupants' energy-saving behaviours by integrating Agent-Based Modelling (ABM), Systems Dynamics (SD), and Building Information Modelling (BIM). The occupant behaviour within this hybrid model is built based upon the theory of reasoned action. Moreover, while most of the ABM studies related to occupant behaviour are based on synthetic data, this study used real energy data collected from customized sensors to validate the proposed model. As a result, it has been shown that adjustment of interior layout (i.e., occupant intervention) can improve building energy performance by 14.9%. In terms of model validation, the proposed hybrid model has exhibited an acceptable level of accuracy with an average CV(RMSE) of 10.5%, MBE of 1.5%, and R² of 0.77. This study differs from other existing studies in that it adopts an interior layout-based human behavioural investigation considering stochastic attitudes and subjective norms of occupants and provides a robust validation through empirical-based intervention.
... Existing studies have indicated significant discrepancies between simulated energy use from building energy models and actual measured data. Building performance gap between the predicted and actual energy use is identified as one of the top ten challenges in the building simulation field (Hong et al. 2018). A few related studies on the performance gaps and exploration on potential reasons leading to the gaps were reviewed. ...
Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’ monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.
... Typical BPS processes consist of model parameters and the simulation engine, where weather data is one of the mandatory inputs. Even though BPS has gone through rapid development and increasing implementation over the past few decades, the handling of input uncertainties is still one of the major challenges facing the accurate prediction of building performance (Wang and Zhai, 2016;Hong et al., 2018). There are two common aspects in BPS that lead to this challenge. ...
Conference Paper
Full-text available
Future overheating risk in dwellings can be potentially mitigated by minimising the variability of overheating hours against uncertainties in future climate via robust optimisation. However, the estimation of this variability value through the utilisation of percentile-based probabilistic weather data has yet to be sufficiently investigated. In this simulation-based study, the bootstrap method is used to quantify the accuracy of the variability estimation via percentilebased weather data. The results indicate significant overheating risk in regulation-compliant houses. An increased degree of difficulty is also suggested in obtaining accurate estimations when considering time periods further in the future and when assuming higher carbon emissions. In addition, the skew normal distribution can be used for a simpler and faster estimation, but the underlying uncertainties must be strengthened throughout its implementation.
A building envelope has a significant influence on overall energy consumption of a building. Presently, Building Information Modelling (BIM) and Building Performance Simulation (BPS) tools are advanced and cost-efficient options for analyzing and improving the energy-efficient building envelop., However, existing BIM-BPS-based studies incorporate several buildings envelop parameters that account for static occupancy profiles. Thiscontributes to higher discrepancies between the actual and simulated energy consumption. Also there is a dearth of studies about the hospital and restaurant building envelop by considering the local construction materials. Thus,the key aim of this study is to perform a stochastic occupant-centric building energy assessment for hospital and restaurant building envelop using agent based modelling (ABM) and BIM-BPS tools. Initially, stochastic occupancy, HVAC and lighting ON/OFF profiles were generated using a robust ABM-BIM approach followed by the real-time data gathering for verifications and energy model construction. Afterwards, the building energy performance was evaluated using the BPS tools, while initial energy consumption data was calibrated using the pre-installed smart meter data in Chittagong, Bangladesh. The study also empowers the assessments of CO2 reduction, carbon credits, life cycle, and payback time analysis from the sustainable built environment perspective. The findings show the highest amount of monthly energy-saving for the hospital (Case 3) and restaurant (Case 3) buildings around 37.42 % (July) and 32.52 % (October), respectively, while considering the local envelop materials. Moreover, the maximum amount of CO2 reduction was 17.71 tons/year and 12.31 tons/year, and the equivalent earned carbon credits were 88,539.5 BDT/year and 61,524.1 BDT/year for hospital and restaurant buildings respectively.. Additionally, the study calculates the payback period for both buildingsby considering different local envelop materials.. The findings of this study have presented a new framework between ABM, BIM and BPS based building envelop simulation, and contribute to proper building envelop material selection in low-income cultural context. Lastly, the study has provided areas for further studies such as the need for comprehensive model construction process, seasonal occupancy patterns, climatic factors, composite materials study for different envelop system which serves as limitations of this current study.
Full-text available
This study develops a model to study household energy use behavior that can impose common preferences for feasible demand estimation with multiple discrete technology choices and multiple continuous energy consumption uses. The model imposes fixed proportions production and additivity of uses for plausible estimation feasibility while adopting a second-order translog flexible functional form to focus on flexibility in identification of consumer preferences that determine interactions among energy uses and between short-run and long-run choices. Using a unique household-level dataset from California, the model is applied to estimate short-run household demand for electricity and natural gas and the long-run technology choices with respect to clothes washing, water heating, space heating, and clothes drying. The estimation results support commonality of underlying preferences except in one case that is explained by an unavailable variable.