Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Procedia Engineering 146 ( 2016 ) 32 – 39
1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of CCHVAC 2015
doi: 10.1016/j.proeng.2016.06.349
ScienceDirect
Available online at www.sciencedirect.com
8th International Cold Climate HVAC 2015 Conference, CCHVAC 2015
A review: simple tools for evaluating the energy performance in
early design stages
Liwei Wena,
*
, Kyosuke Hiyamaa
aFaculty of Engineering, Yamaguchi University, 2-16-1, Tokiwadai, Ube-shi, Yamaguchi, 755-8611, Japan
Abstract
To make performance-based design decisions, an energy performance evaluation should be integrated into the design process.
Simple tools can provide easy and rapid performance analysis without requiring detailed information as inputs. This paper presents
a review of ten simple tools regarding their application, characteristics, modeling/calculation features and outputs. The key
discussion focuses on the establishment of the necessary inputs. This study intends to clarify the effective approach of establishing
those necessary inputs and find out the obstacles in this process. Note that building properties (BP) templates can extensively
simplify the setup of construction data, internal load data and conditioning data. The lack of a method to efficiently establish energy
setting parameters is recognized as the obstacle in the process of BP data setup. Reusing Building Information Model (BIM) data
of past well-designed projects is expected to be a good approach to overcome that obstacle.
© 2016 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the organizing committee of CCHVAC 2015.
Keywords: Type your keywords here, separated by semicolons ;
1. Introduction
Increasing requirements of sustainability has prompted architects and engineers to pay more attention to the energy
performance of their design. The design decisions, such as building form, orientation, fenestration, and construction
materials, made in the early design stages have the most impact on the building energy performance [1]. This impact
was also confirmed by the simulation in which intelligent facades was integrated into the design process for hot
* Corresponding author. Tel.: +81-0836-85-9730; fax: +81-0836-85-9730.
E-mail address: u501we@yamaguchi-u.ac.jp
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of CCHVAC 2015
33
Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
climates [2]. Thus, a preliminary energy performance evaluation must be performed during the early design stages to
enable architects to make performance-based design decisions. American Institute of Architects [3] published the
design guideline that also emphasizes the importance of energy performance prediction in the early design stages
(named as Design Performance Modeling (DPM)). This guideline summarized DPM as being a less complex and less
time consuming approach that allows for rapid exploration of many design parameters. Furthermore, the analysis
results of DPM are not overly specific, generally providing architects with a range of energy performance that are
dependent on the design decision variations. Thus, suitable tools are needed to implement the energy performance
evaluation in the early design stages.
In the past few years, building performance simulation (BPS) has experienced a rapid development. The US
Department of Energy [4] created the building energy software directory, which lists more than 400 BPS. Crawley [5]
compared the characteristics and capacities of twenty widely applied BPS. Most of the BPS were found to focus on
predicting the whole-building energy performance after the design. However, some studies indicated that performing
BPS was still beneficial to support early decision making. For example, BPS was utilized to assist in the generation of
the shape of the building with the minimum energy consumption [6]. The utilization of BPS can obtain an accurate
prediction results. However, a BPS usually requires a large number of inputs. Establishing those inputs for a BPS is
quite time consuming [7]. Another limitation of implementing a BPS in the early design stages is the requirement of
detailed information as input. Such detailed information can be acquired only in design development stages or later.
Note that the quality and reliability of inputs greatly affect the simulation results. Unreliable inputs may cause
questionable simulation results that produce incorrect information for decision making [8]. In addition, good
understanding of the thermal processes and the simulation tools is critical to perform a BPS. However, in early design
stages, the performers are usually not professional engineers but architects. These architects have limited knowledge
in this professional field. Thus, despite the rapid development in the application of BPS, architects still suffer from the
barriers of utilization and selection of such complex BPS [9].
Simplified energy performance prediction tools (simple tools) can provide easy and quick performance analysis,
thus assisting architects in considering design alternatives. Simple tools do not require much detailed information as
inputs. In combination with the characteristics of DPM mentioned above, simple tools are regarded as a very useful
approach to perform preliminary energy performance prediction and inform early design decisions. This paper presents
a review of ten simple tools for evaluating energy performance in the early design stages. The ten simple tools are Cf,
PAL*, ERED, WinSim, TEMMI/MCI3, FACES, BuildingCalc, CooLVent, DPV and EcoDesigner. The key
discussions focus on the necessary inputs of the ten simple tools. This study intends to clarify the effective measures
of establishing those necessary inputs and find out the obstacles in this process.
2. Overview of the ten tools
The ten simple tools reviewed in this paper can be divided into three categories: design index, simple analysis
program and BIM relevant program. Design index tools, including Cf, PAL* and ERED, utilize the performance about
envelope to indicate the approximate energy performance of the buildings. A simple analysis program usually exports
specific calculation results, such as thermal load, indoor air temperature and airflow rate. However, a simple analysis
program requires relatively more inputs. WinSim, TEMMI/MCI3, FACES, BuildingCalc and CooLVent are examples
of simple analysis programs. A BIM relevant program combined with BIM software can automatically generate the
energy model. Examples of BIM relevant programs are DPV and EcoDesigner.
2.1. Cf (shape coefficient)
Cf was designed as an indicator of the heating consumption for residential buildings in a rigorous climate [10]. Cf
is defined as the rate between the external envelope surface area and the inner volume of the building. The authors
performed the trend test on several theoretical buildings and determined that Cf was proportional to heating
consumption. Therefore, outdoor temperature is the largest driver for heating consumption of the residential building
in rigorous climate. However, there is no correlation between Cf and heating consumption in mildest and sunny climate.
34 Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
2.2. PAL* (Perimeter Annual Load*)
Ministry of Land, Infrastructure, Transport and Tourism, Japan created PAL* to describe the thermal insulation
performance of the building envelope for non-residential buildings [11]. PAL* is defined as the rate between the total
annual load of the perimeter zone and the area of the perimeter zone of the building. In Japan, the energy convention
plan must be proposed for non-residential buildings whose area exceeds 2000 m2. PAL* is adopted as an index to
quantify the energy conservation level. The standard value of PAL* was set according to the application and location
of the project. The calculated PAL* for the target building should be smaller than the standard value.
2.3. ERED (Envelop-Related Energy Demand)
The design indicator ERED was developed to indicate the energy consumption of residential buildings [12]. The
limitation of Cf, i.e., it has no correlation with the energy performance in a mildest climate, is addressed by ERED. It
is the sum of the heat transmission through the envelope and the solar gains through the windows calculated using
some simple formulas and definitions. This indicator is combined with the envelope shape, envelope materials and
window areas.
2.4. WinSim
To facilitate the selection of windows, WinSim was developed to quickly and easily evaluate the thermal
performance of windows [13]. An electric analogy model (two-node) is adopted in this program. The calculation
object is limited to a simple one-zone room. The outputs of WinSim are the annual energy consumption, the amount
of transmitted solar energy and total number hours when the building is under overheated conditions.
2.5. TEMMI/MCI3
This commercial program TEMMI/MCI3 is used to perform thermal analysis for non-residential buildings [14].
This program intends to design building envelope with the minimum thermal load. This program uses an electric
analogy model (three-node) for performing the thermal analysis. The outputs include thermal load and indoor air
temperature. To provide feedback to architects, the program performs an automatic comparison among envelope
design alternatives on the basis of the thermal load.
2.6. FACES (Forecasts of Air-Conditioning System’s Energy, Environment, and Economical Performance by
Simulation)
FACES is a simplified simulation program for choosing a suitable heat source system [15]. The program couples
the dynamic thermal load calculation and the HVAC system simulation. Automatic design algorithms of the building
model and the HVAC system are introduced into FACES. The program is equipped with five building models and
thirteen heat source systems for selection. The outputs are the peak load, the annual thermal load, the amount of energy
consumption, the amount of carbon emissions and the cost.
2.7. BuildingCalc
BuildingCalc was developed to estimate the thermal load and the indoor environment for non-residential buildings
[16]. The program is based on WinSim. Thus, the model and calculation objects are the same as those of WinSim.
The solar gains through the windows are thoroughly described. Beyond the thermal load, the program can also
examine the effects of shading, venting and mechanical ventilation. The program exports the thermal load, the indoor
air temperature, the hours of overheating. It can also export the value of Predicted Percentage of Dissatisfied (PPD)
and Predicted Mean Vote (PMV).
35
Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
2.8. CoolVent
CoolVent was developed to evaluate the performance of natural ventilation [17]. A multi-zone coupled thermal
and airflow model is used to predict the zone temperature and the direction, the temperature and the rate of airflow.
The program can perform both transient (24-hour period) and steady calculations. Architects can understand how
some design parameters affect the internal temperature and airflow and confirm the measurable effects from the design
of natural ventilation.
2.9. DPV (Design Performance Viewer)
DPV was combined with the BIM software (Revit from AUTODESK) to access the geometry inputs [18]. DPV
mainly focuses on the heating demand of buildings. A mathematical model was used to perform energy and exergy
analysis. The concept of exergy is defined as the potential of an energy source dispersing from heat generation to
emission into the room. The analysis results of DPV are the energy balance and exergy balance of selected heat chain.
DPV can assist architects in making a balance among building form, building material and technical systems in the
early design stages.
2.10. EcoDesigner
GRAPHISOFT developed EcoDesigner as an add-on application operating inside of ArchiCAD [19]. Multiple
thermal models can be automatically generated in the design environment. EcoDesigner can evaluate key design
decisions, such as materials, glazing and ventilation. The output of EcoDesigner are the yearly energy consumption,
the carbon emissions and the monthly energy balance.
3. Discussion of necessary inputs of ten tools
To promote the utilization of simple tools in design practice, the necessary inputs and whether those necessary
inputs can be easily acquired or not must be considered. Table 1 presents the necessary inputs of the ten simple tools
considered in this study. FACES, DPV and EcoDesigner can also consider the HVAC system to further calculate
specific performance of buildings. However, the use of the thermal load as the objective function is focused on in this
study. Thus, only the inputs related to the thermal load are listed for these three tools. The collected necessary inputs
are divided into three large categories: weather data, building geometry data and building properties (BP) data. BP
data are defined as the non-geometrical inputs required by energy models [20].
Both simple analysis programs and BIM relevant programs use some approaches based on the hourly climate data
to represent the standardized annual climate. Thus, users need to know only the location of their project. ERED is the
only design index tool requiring simple climate inputs. These required inputs can be easily obtained from the “epw”
climate file format that is made freely available to the public by the DOE [21]. Regarding geometry data, a simple
method with the capacity of converting 2D building models to the representative “shoebox” thermal models has been
described [22]. In addition, the “simulation domain model” is able to exchange data between the 3D BIM software
and the simulation domain and makes it possible to directly acquire building information to reduce the efforts required
to handle geometry inputs [23]. The huge progress of typical meteorological years and the BIM technique extensively
simplify the setup of weather and geometry inputs. Hence, an effective solution to set up BP data is urgently required.
BP data can be further divided into general data, construction data, internal load data and conditioning data. General
data represent the building application and site information, including terrain and surrounding building. A majority of
construction data are used to describe the building material properties, which are specified as the properties of opaque
envelope and glazing. The rest of the construction data are used to describe the dimensions of the openings. The
dimensions of the openings are expressed as the window-to-wall ratio (WWR). Internal load data are composed of the
internal heat gain density and their corresponding schedules. Conditioning data are composed of two groups. One
group comprises the indoor environment controlling data to maintain the comfortable level of occupants. The other
group comprises the passive design strategies, such as ventilation and shading devices. General data can be completely
obtained from the client demand, even during the very early design stages. Besides general data, a part of internal load
36 Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
Table 1. Necessary inputs for ten tools
Design index
Simple analysis program
BIM relevant
program
Cf
PAL*
ERED
WinSim
TEMMI/MCI 3
FACES
BuildingCalc
CooLVent
DPV
EcoDesigner
Weather data
Location
○
○
○
○
○
○
○
Outdoor temp.
○
Wind speed
○2
Wind direction
○2
Solar radiation
○
Building
geometry data
Orientation
○
○
○
○
○
○
○
○
○
Volume
○
○
○
○
Number of floors
○
○
○
○
○
Floor dimensions
○
○
○
○
○
○
Interior wall length
○
Floor areas
○
○
○
○
○
○
○
○
Exterior wall areas
○
○
○
○
○
○
○
○
Internal wall areas
○
○
○
Roof areas
○
○
○
○
Ground floor areas
○
○
○
Window areas
○
○
○
○
○
○
Atrium dimensions
○
○
Building properties data
General
data
Application
○
○
○
○
○
Terrain information
○
○
Surrounding
building
○
○
○
○
Construction data
Exterior wall
properties
○
○
○
○
○
○
○
○
○
Roof properties
○
○
○
○
○
○
floor properties
○
○
○
○
Interior wall
properties
○
○
○
Thermal capacity of
construction
○
○
○
Glazing properties
○
○
○
○
○
○
○
○
Frame properties
○
○
SHGC
○
○
○
○
○
SHGCWS1
○
Exterior wall /roof
color
○
WWR
○
○
○
○
Internal load data
Occupation density
○
○
○
○
○
Occupation schedule
○
○
○
Equipment density
○
○
○
○
○
Equipment schedule
○
○
○
Lighting density
○
○
○
○
○
Lighting schedule
○
○
○
Total internal load
○
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Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
Table 1. Necessary inputs for ten tools (continued)
Design index
Simple analysis program
BIM relevant
program
Cf
PAL*
ERED
WinSim
TEMMI/MCI 3
FACES
BuildingCalc
CooLVent
DPV
EcoDesigner
Building properties data
Conditioning data
Heating set points
○
○
○
○
○
Cooling set points
○
○
○
○
Heating season
○
○
Cooling season
○
○
Over temp. limit
○
Initial building
temp.
○
Blind
○
○
Artificial lighting
type
○
Ventilation type
○
○
Window opening
setting temp.
○
Window opening
parameters
○
○
Mechanical
ventilation rates
○
○
○
Natural ventilation
rates
○
Infiltration rate
○
○
○
Shading device
○
○
○
○
1.Solar heat gain coefficient of the windows with shading device on
2.Users need to define this values when performing a steady calculation
Fig. 1. Reliable sources of building properties data
data and indoor environment controlling data within the conditioning data also depend on the client demand. The
establishment of remnant BP data can be greatly simplified by BP templates [20]. The BP templates proposed for BPS
are defined as a comprehensive database of the thermal zone attribution data. The utilization of BP templates can
38 Liwei Wen and Kyosuke Hiyama / Procedia Engineering 146 ( 2016 ) 32 – 39
greatly reduce the time consumption and the risk of error. The predefined data in BP templates mainly originate from
the reference design guideline and experience. Building material properties data in construction data and the remnant
indoor environment controlling data in conditioning data can be precisely obtained through the BP templates. It is
explained that such data in BP templates are from the design guideline. All of the BP data mentioned above can be
acquired from reliable sources, as shown in Figure 1.
However some data without reliable sources also exists. Those data are generally determined by experiences, such
as WWR, belonging to construction data, and shading device and ventilation strategies, belonging to conditioning
data. The lack of measure to efficiently and rapidly establish those data is recognized as the obstacle in the process of
BP data setup. The target percentage glazing, shade depth, target percentage skylight and conceptual construction-
mass glazing were reported to be very sensitive to the predicted results and should be carefully given in the early
design stages. Such parameters are called energy setting parameters [24]. Energy setting parameters cannot be solely
determined by uniform standards or design guidelines. The issues involved with energy setting parameters are
highlighted by the example of setting up the WWR. The ANSI/ASHRAE/IESNAI Standard 90.1-2010 recommends
the maximum value of WWR should be 40%. However, the analysis results of case studies indicated that the optimal
WWR was greatly influenced by the building design conditions, such as the building scale, location, and operation
mode (daylighting and natural ventilation). For a building with a relatively large floor area and space depth, the largest
WWR was found to be the best solution [25]. This result contradicts the suggested value by ASHARE. As a
consequence, an effective measure must be developed to determine energy setting parameters that cannot be
determined by a regular standard. One feasible method is to reuse the BIM data of past well-designed projects to
generate optimal default values during the early design stages [26].It is critical to find the link between a new project
and past projects for such data inherit. This link refers to not only the building configuration but also the operation
mode. Therefore, the exploration of design conditions sensitive to energy setting parameters is necessary to generate
robust default values. The authors plan to perform relevant studies regarding this issue in the future.
4. Conclusions
To inform early design decisions, we reviewed ten simple tools for energy performance evaluation in the early
design stages. The application, characteristics, modeling/calculation features and outputs of ten simple tools were
examined. The necessary inputs for implementing these ten simple tools were summarized. The setup of BP data was
sufficiently discussed. The general data and a part of the internal load data mainly depended on client demand.
Furthermore, the application of BP templates was highlighted as a means to extensively simplify the setup of
construction data, internal load data and conditioning data. However, some data, such as WWR, shading devices, and
ventilation strategies cannot be determined by a regular standards or design guidelines. The lack of measures to
efficiently and rapidly establish those data is recognized to be an obstacle in the BP data setup process. Reusing BIM
data of past well-designed projects is considered to be an effective approach. To optimize such inherited data, it is
critical to find the link between a new project and past projects. This link refers to not only the building configuration
but also the operation mode. Therefore, the exploration of design conditions sensitive to energy setting parameters is
necessary to generate robust default values.
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