ASHRAE and IBPSA-USA SimBuild 2016
Building Performance Modeling Conference
Salt Lake City, UT
August 8-12, 2016
CITY SCALE MODELING WITH OPENSTUDIO
Daniel Macumber,1 Kenny Gruchalla,2 Nicholas Brunhart-Lupo,2 Michael Gleason,3 Julian
Abbot-Whitley,3 Joseph Robertson,4 Benjamin Polly,4 Katherine Fleming,1 and Marjorie Schott1
1Commercial Buildings Group, National Renewable Energy Laboratory, Golden, CO
2Computational Science Center, National Renewable Energy Laboratory, Golden, CO
3Strategic Energy Analysis Center, National Renewable Energy Laboratory, Golden, CO
4Residential Buildings Group, National Renewable Energy Laboratory, Golden, CO
Assessing the impact of energy efficiency technologies
at a district or city scale is of great interest to local
governments, real estate developers, utility companies,
and policymakers. This paper describes a flexible
framework that can be used to create and run district
and city scale building energy simulations. The
framework is built around the new OpenStudio City
Database (CityDB). Building footprints, building
height, building type, and other data can be imported
from public records or other sources. Missing data can
be inferred or assigned from a statistical sampling of
other datasets. Once all required data is available,
OpenStudio Measures are used to create starting point
energy models and to model energy efficiency
measures for each building. Together this framework
allows a user to pose several scenarios such as “what if
30% of the commercial retail buildings added rooftop
solar” or “what if all elementary schools converted to
ground source heat pumps” and then visualize the
impacts at a district or city scale. This paper focuses on
modeling existing building stock using public records.
However, the framework is capable of supporting the
evaluation of new construction, district systems, and the
use of proprietary data sources.
The amount of information collected about the built
environment is increasing every day. The potential to
extract value from these data increases as more data are
collected and made available. Local governments hope
to use this information to meet energy performance
goals at the city level. Real estate developers hope to
design net zero energy districts. Utilities hope to better
target incentives that will save more energy for less
money. Local policymakers hope to know how new
rules and regulations will impact energy use and
greenhouse gas emissions.
In order to provide these stakeholders with the data that
they need, many cities are adopting open data policies
that aim to make public data as accessible as possible.
One significant new development is the adoption of
energy use disclosure laws, which require that certain
buildings publicly disclose their energy use. As the
value of this energy use data becomes more clear
(Krukowski 2014) and the challenges of making energy
use information public are addressed (Krukowski and
Majersik 2013), local governments are adopting
positions that are pro-public disclosure of energy
information (NASEO 2015).
As more information becomes publicly available and
computational resources continue to become more
affordable, it is no surprise that detailed modeling at the
district and city scales is becoming more common for a
wide range of analyses. Issues such as transportation,
air quality, urban heat island effect, electrical power
distribution, and building energy performance can now
be examined in new ways. In the case of urban building
energy modeling (UBEM), it appears that the trend is
towards bottom-up modeling in which each building is
modeled individually. Trends and simulation
methodologies for this bottom-up UBEM are explored
in detail by Cerezo and Cristoph (2016).
Several UBEM software solutions have been
developed. In Germany, an energy simulation of more
than 14,000 buildings in the city of Ludwigsburg was
performed (Nouvel et al. 2014) using the ISO 13790
heat balance algorithm (CEN/ISO 2008). CitySim
(Robinson et al. 2009) simulates building energy using
a simplified energy model and plans to incorporate
water, transportation, and urban climate modeling in the
future. The urban modeling interface (UMI) tool
(Reinhart et al. 2013) performs operational energy,
daylighting, and walkability evaluations of complete
neighborhoods using EnergyPlus (Crawley et al. 2000)
and Radiance (Larson and Shakespeare 1998). The
This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.
work discussed in this paper is different from the work
by Nouvel et. al. and CitySim in that this work uses the
detailed simulation engines EnergyPlus and Radiance
rather than simplified models. While this work and the
UMI tool both use EnergyPlus and Radiance, UMI’s
user interface depends on the commercial Rhino
software, whereas the user interface developed in this
work is open source and freely available. Finally, this
work is unique in that it is designed to provide a
flexible, open source framework that others can use to
implement custom district or city scale modeling
The OpenStudio City Modeling Framework described
in this paper is designed to enable rapid development of
district and city scale energy modeling applications, in
much the same way that the OpenStudio Software
Development Kit (SDK) was designed to enable rapid
development of energy modeling tools (Weaver et al.
2012). This paper describes the process of assembling
open source projects into a framework that can be
easily used for city scale modeling. This framework
uses the existing OpenStudio Analysis (Long et al.
2014) project. The new OpenStudio City Database
(CityDB) stores district and city scale building
information. Just as the OpenStudio Application is a
demonstration of the OpenStudio SDK, a reference
implementation of the OpenStudio City Modeling
Framework is available to jumpstart new projects and
can easily be customized.
Figure 1 OpenStudio City Modeling Framework
An overview of the OpenStudio City Modeling
Framework is shown in Figure 1. The workflow for
using this framework is outlined below, and each step is
further explained in subsequent sections:
1. Building data, from public records or other sources,
are collected in GeoJSON format. Cleanup scripts
align terms with the CityDB schema and infer
2. Building data in GeoJSON format is uploaded to the
CityDB using a simple Web interface.
3. An OpenStudio Analysis describing workflows to
simulate baseline buildings as well as energy-
efficient alternatives using OpenStudio Measures
(Roth et al. 2016) is developed using the Parametric
Analysis Tool and uploaded to the CityDB using the
simple Web interface.
4. Scenarios are created that assign specific design
alternatives to each building in the CityDB. These
design alternatives are simulated by applying a
series of OpenStudio Measures to generate the
baseline building and to model energy efficiency
options. Simulation results are pushed back to the
CityDB. If desired, simulation results can also be
pushed to a DEnCity database (Roth et al. 2012).
5. If the scenario includes district systems, a separate
simulation is run for each district system. The
district system analysis includes a system creation
measure that pulls time-series of loads for each
building on the system from the CityDB and
assembles an energy model of the district system.
The district system model is simulated and results
are pushed to CityDB and, if desired, DEnCity.
6. After simulations are complete, a scenario exporter
gathers simulation results for each building and
district system in the scenario and exports a
GeoJSON file containing simulation results.
INPUT DATA SOURCES
The first step in applying the OpenStudio City
Modeling Framework is to gather building information
for the regions of interest. This initial work is focused
on using publicly available data for existing buildings.
Public records were collected for three cities: San
Francisco, California; Denver, Colorado; and Portland,
Oregon. All three cities are following a nationwide
trend to increase availability of public data while
encouraging third parties to build applications on top of
this data. San Francisco has an open city data initiative
with more than 340 public datasets (City and County of
San Francisco 2016). Denver has 203 public datasets
(City and County of Denver 2016). Portland has an
open data initiative (City of Portland 2016) with 149
public datasets. For this initial work, the only datasets
considered for each city were the building footprint
datasets (City and County of San Francisco 2012),
(Denver Regional Council of Governments 2014), (City
of Portland 2013). A selection of building footprints
available for the city of Portland, Oregon, is shown in
This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.
Figure 2 Portland building footprints
Merging these footprint datasets with other datasets
(either public or proprietary) would result in richer sets
of information that would support more detailed and
accurate energy models. One dataset of particular
interest is San Francisco’s dataset of publicly disclosed
energy usage for select commercial buildings (SF
Environment 2016). The Standard Energy Efficiency
Data (SEED) Platform™ (Alschuler et al. 2014) has
been developed specifically for cities to manage this
type of disclosed energy data. Furthermore, SEED
provides many data cleansing and merging features that
would be ideal for merging multiple datasets. However,
merging multiple datasets was out of the scope of this
Each footprint dataset was evaluated for the minimal
pieces of information required for energy simulation.
Building footprint is required to generate the overall
shape and size of the building. Building area is required
to capture the useful area within the building. Building
height is required to capture the building volume and
exterior wall area. The number of stories is related to
building height and floor area. Building type is required
to assign interior loads and schedules. Building address
is not required to perform an energy simulation.
However, building address is often a key identifier used
to join multiple datasets.
If some of the information required for simulation is
missing, it can be estimated using other information. If
the floor area is not available, the number of stories
may be estimated based on building height. If the
number of stories is not available, the ratio of floor area
to footprint area can serve as a surrogate. If the number
of stories is not known it can be estimated from the
building height. If building area is not available it can
be estimated using footprint area and number of stories.
If building type is not available, it can be inferred using
zoning, building size, or other information.
Table 1 Summary of public data for three cities
1 Complicated by merged footprints
2 Not directly available, inferred from zoning data
As shown in Table 1, footprint data from each city
contained the minimal information to support
simulation. The San Francisco building footprints
offered one particular challenge that the other cities did
not have. Close inspection found that the footprints in
the San Francisco dataset often spanned tax lots and
individual footprints actually represented multiple
buildings. It is likely that the footprint generation
process merged adjacent buildings due to the close
proximity of buildings in San Francisco. To separate
these merged footprints, the footprints in the dataset
were intersected with tax lot boundaries and the
resulting shapes were taken to represent individual
buildings. All of the original footprints in the San
Francisco dataset included minimum and maximum
roof height information generated by Light Detection
And Ranging (LiDAR) measurements. However, this
information was only available for the merged
footprints and was not available for individual
buildings. The hilly topology of San Francisco further
compounded this problem as a significant portion of the
elevation gain over a merged footprint could come from
change in elevation of the street.
Detailed building type information was not present in
the footprint data for any city. However, zoning
information (commercial, residential, open space, etc.)
was available for all cities along with identifiers
separating real buildings from other features (e.g.,
sheds, water tanks, etc.). Several approaches to infer
more detailed building type from the available
information are possible. One option explored was to
separate the buildings into commercial and residential
buildings and take a statistical sampling of the same, or
slightly higher, number of buildings from the
Commercial Buildings Energy Consumption Survey
(CBECS) (EIA 2012) or the Residential Energy
Consumption Survey (RECS) (EIA 2009). The
buildings in the statistical sample were then assigned to
the buildings in the floorprint dataset such that the
difference in floor area was minimized. Alternatively,
datasets from proprietary sources such as CoStar
This is a work of the U.S. Government and is not subject to copyright protection in the United States. Foreign copyrights may apply.
(CoStar 2016) could be merged with the building
footprints to provide building type information.
After reviewing all the footprint datasets, the authors
concluded that Portland had the best footprint dataset of
the three cities investigated in this work. The Portland
building footprints did not have the same issues with
merged footprints that San Francisco did. All of the
buildings in the Portland dataset had detailed zoning
information (e.g., high-density residential, low-density
residential, etc.). A total of 1,921 buildings were
sampled in Portland as shown in Figure 2. Of these, 855
are residentially zoned, 646 are commercially zoned,
394 are industrial, 15 are open space, and 11 are zoned
as mixed commercial and residential. Eight hundred
seventy-two buildings have the number of residential
units defined (ranging from 1-284). However, many
buildings with residential units are commercially zoned
rather than mixed commercial/residential. A large
percentage of buildings, 92%, had address information.
Most buildings, 99%, had information about the
number of stories (ranging from 1-42). All of the
buildings had floor area information and 96% of the
buildings also had height information.
Because buildings in the Portland dataset had address
information, it was easy to spot check information
about particular buildings. However, these spot checks
revealed that information in the dataset was not always
accurate. For example, the number of stories for single
family homes often appeared incorrect when looking at
street level imagery. The Portland dataset included
more than the minimal amount of information to
support energy simulation. This additional information
can be used to identify buildings that may contain
incorrect information. These buildings can be flagged
for manual inspection before simulation.
OPENSTUDIO CITY DATABASE
The OpenStudio City Database (CityDB) is central to
the OpenStudio City Modeling Framework. The
CityDB is a NoSQL Mongo database with a RESTful
Application Program Interface (API). Because the
Mongo database does not have a strict schema, building
properties can be customized for each instance of the
OpenStudio City Modeling Framework. However, these
properties must be coordinated with the set of
OpenStudio Measures used for building model
articulation and modeling energy efficiency measures.
To facilitate this coordination, the building properties
can be specified with a JSON schema defining the
properties that are allowed for each structure as well
whether each property is required or optional. The
reference implementation defines one such schema, but
this may be customized for any other instance of the
framework. When possible, property names should be
aligned with entries in the Building Energy Data
Exchange Specification (BEDES) (Mercado et al.
DATA TRANSFER FORMAT
Footprint data for all three cities was available in ESRI
Shapefile format. The shapefile format is widely used
but is not ideal for data processing. Therefore, data was
exported from the footprint datasets to an intermediate
file format for data processing and transfer to the
OpenStudio CityDB. For city scale applications,
geographical information needs to be included with the
other building physical and energy properties. There are
two prominent file formats capable of transferring
building properties with geographical data structures:
CityGML and GeoJSON. Both of these formats were
considered as potential data transfer formats for the
CityGML (Kolbe et al. 2005) was investigated as a
potential data transfer format, especially given the
development of the Energy Application Domain
Extension (ADE) (Wate and Saran 2015). However, the
development of the Energy ADE is subject to a
standards process and fairly inflexible, so it would not
suit the need to provide custom data for different
applications. Additionally, CityGML is not as widely
supported in the United States as it is in Europe. If
CityGML becomes a more widely adopted standard,
then it could be added as an additional import/export
format for CityDB.
The GeoJSON format was chosen as the data transfer
format for the OpenStudio City Modeling Framework.
GeoJSON is a widely used format for encoding a
variety of geographic data structures as well as a
flexible set of properties for each structure. GeoJSON
supports the following geometry types: Point,
LineString, Polygon, MultiPoint, MultiLineString, and
MultiPolygon. The Coordinate Reference System is an
additional feature that easily describes the data’s
geographic coordinate reference system. At the time of
this writing, GeoJSON is supported by numerous
mapping and GIS software packages, including
OpenLayers, Leaflet, MapServer, Geoforge software,
GeoServer, GeoDjango, GDAL, Safe Software FME,
CartoDB, PostGIS, Mapnik, Github, Bing Maps,
Yahoo! Maps, and Google Maps. Several databases,
including MongoDB, support queries based on
For this work, footprint data was exported from the
original shapefile to many GeoJSON files, with one
GeoJSON file per census tract. This resolution provided
a nice balance between file size and the amount of
content contained in each file. After exporting to
GeoJSON, data cleaning scripts were run on each
GeoJSON file. These scripts take a GeoJSON file as
input and write a modified GeoJSON file as output.
Additionally, these scripts map property names from
those in the original data set to those in the CityDB
schema. Finally, these scripts infer missing required
data. Properties that are inferred are marked as such.
Because GeoJSON is a widely supported format, many
tools already exist for generating, inspecting, and
editing data in GeoJSON format. One such tool is freely
available at http://geojson.io. This website allows a user
to upload a GeoJSON file, plot the data on a map, view
and edit properties, and save updated files. The authors
found this website very useful for inspecting raw
GeoJSON files exported from the public datasets as
well as for inspecting cleaned GeoJSON files written by
the data processing scripts. The website also allows
users to delete existing features, modify vertices, and
create new footprints. While not used in this work,
these features could be leveraged for new construction
Figure 3 Portland footprints in geojson.io Web
Like many applications built on OpenStudio, the
OpenStudio City Modeling Framework uses
OpenStudio Measures heavily. OpenStudio Measures
are small scripts that automate portions of the energy
modeling workflow (Hale et al. 2012). These scripts
conform to a specific interface (NREL 2014) that takes
an energy model as well as user arguments as input.
The scripts leverage the OpenStudio Ruby API to alter
the energy model and the output is a modified energy
model. OpenStudio Measures can be chained together
to implement a complete building energy modeling
The reference implementation includes OpenStudio
Measures that can generate a starting point building for
each building in the dataset. The first measure
constructs building geometry from the data in a
GeoJSON file. This measure has the option to include
surrounding buildings as shading surfaces. If the
GeoJSON structure is of type Polygon or Multipolygon,
this geometry is used as the building footprint and
extruded up. If the structure is of type Point, then basic
box geometry is created. Methods that convert latitude
and longitude in the WGS84 (NIMA 1997) coordinate
system to and from a local Cartesian coordinate system
have been added to the OpenStudio SDK to support this
work. Figure 4 shows OpenStudio geometry created
from the dataset shown in Figure 3. The building of
interest is modeled in full detail while surrounding
buildings are included for shading purposes only.
Figure 4 OpenStudio geometry for Portland dataset
Once the geometry has been generated, building type
information is used to generate other energy modeling
content. A building type measure is included in the
reference implementation, which covers both
commercial and residential buildings. For commercial
building types, the baseline automation features of the
OpenStudio standards gem are used to generate an
ASHRAE 90.1 Appendix-G compliant baseline model.
For residential buildings, a series of OpenStudio
Measures implementing logic originally written for the
BeOpt software (Christensen et al. 2006) are applied to
generate a baseline building. Other OpenStudio
Measures for modeling energy efficiency measures are
left to the user. Examples include lighting retrofit,
elevator retrofit, install solar photovoltaics, etc.
OpenStudio Measures can be found in the Building
Components Library (Fleming et al. 2012) and users
can also write their own.
After all baseline generation and energy efficiency
OpenStudio Measures have been run, the building
energy model is simulated in EnergyPlus. After the
energy simulation is complete, a series of OpenStudio
Reporting Measures are run. These OpenStudio
Reporting Measures access simulation results to
calculate specific metrics or perform quality control
checks. One OpenStudio Reporting Measure gathers
high level results and sends them back to the CityDB.
Another OpenStudio Reporting Measure pushes time-
series data from the simulation (e.g., hourly electrical
usage) to the CityDB database.
This work leverages the OpenStudio Analysis format to
define and run parametric analysis for each building. If
the simulation includes district systems such as central
chilled water plants, then a separate OpenStudio
Analysis can be specified for each type of system. An
OpenStudio Analysis defines a workflow of
OpenStudio Measures that are applied to a starting
point model. There are two types of OpenStudio
Analysis; algorithmic and manual. Algorithmic
analyses use a sampling or optimization algorithm to
determine which combinations of variables to run.
Manual analyses allow the user to create named
combinations of variables. OpenStudio Analyses are
currently defined using the OpenStudio Analysis
Spreadsheet format. However, work is underway on the
next version of the OpenStudio Parametric Analysis
Tool, which will provide a graphical interface to the
OpenStudio Analysis Format. Users will be able to
define both algorithmic and manual analyses and
perform simulations locally or using cloud resources.
The first version of the OpenStudio City Modeling
Framework uses the manual analysis type. Users define
simulation workflows for named design alternatives
(e.g., “Baseline”, “30% Reduction”, “Net Zero”, etc).
The “Baseline" simulation workflow would include
OpenStudio Measures that construct a baseline building
for simulation. The “30% Reduction” workflow would
include OpenStudio Measures to construct the baseline
building as well as additional OpenStudio Measures to
reduce energy use. The “Net Zero” workflow would
add further OpenStudio Measures to achieve a net zero
performance level. The user then defines named
scenarios in which each building is assigned a named
design alternative. The scenario creation interface is
shown in Figure 5.
Figure 5 Mockup of scenario editor
In the future, the OpenStudio City Modeling
Framework will be extended to support algorithmic
analyses. This will allow buildings to be automatically
calibrated to disclosed energy usage or optimized for
energy given life cycle cost constraints.
After all simulations have been completed, each
scenario can be exported as a GeoJSON file. This
export will include building footprints, input
parameters, and simulation results. Part of this work has
been dedicated to the exploration of state-of-the-art
virtual reality displays to manipulate, visualize, and
analyze output of the OpenStudio City Modeling
Framework. NREL’s Insight Center Visualization Lab
provides a two-surface, optically tracked, stereoscopic
immersive environment. This exploration is motivated
by evidence that suggests interaction with and
understanding of complex spatial data can be improved
in these types of immersive virtual environments
After the simulations are run and scenarios exported to
GeoJSON format, these scenarios may be loaded into
the NREL Insight Center, where the building structures
are presented in three dimensions optionally overlaid
with their energy properties and their time-series
results. While the three-dimensional buildings have a
direct mapping into the space, the selection and
manipulation of the energy properties and simulation
results associated with those structures can be
cumbersome in three-dimensional space. To facilitate
selection and manipulation of these data, we have
integrated a Web server with a RESTful mode into the
immersive visualization application. This allows the
rich immersive environment to be controlled with an
intuitive tablet interface. The aim is to further enhance
researchers’ ability to interact and explore these
scenario data in real time.
Figure 6 Initial NREL Insight Center interface
CONCLUSION AND FUTURE WORK
Bottom-up building energy modeling at the district and
city scale is sure to remain a hot topic in the near future.
The OpenStudio City Modeling Framework provides a
flexible framework that others can use to build custom
city scale modeling applications. Future work includes
integrating CityDB with the SEED Platform. If
disclosed energy usage is publicly available for
buildings, the OpenStudio City Modeling Framework
can use this information to first remove any universal
bias in the modeling assumptions and then to calibrate
individual buildings against past data.
The first application to be built on the OpenStudio City
Modeling Framework is URBANopt (Polly et al. 2016).
URBANopt is being developed to provide a complete
user interface to the building and district system
capabilities discussed in this work. In addition to being
able to import data for existing buildings, URBANopt
will allow users to define floorprints and building
properties for new construction. URBANopt will also
allow users to define district systems on the map to
calculate the length of piping, which is an important
component of the cost for district water systems.
Finally, URBANopt will be able to display results for
scenarios after the simulations are complete. The user
can select a scenario, e.g., “High Performance Schools”
or “2030 Goals,” to export from the CityDB as
GeoJSON. Static values can be overlaid onto building
geometry for annual metrics such as energy use
intensity or carbon emissions. Time-series data can be
explored with an interactive time dial.
This work is supported by the U.S. Department of
Energy’s Office of Energy Efficiency and Renewable
Energy, Laboratory Directed Research and
Development funding at the National Renewable
Alschuler E., Antonoff, J., Brown, R., and Cheifetz, M.
2014. Planting SEEDs: Implementation of a
Common Platform for Building Performance
Disclosure Program Data Management, 2014
ACEEE Summer Study on Energy Efficiency
in Buildings, Pacific Grove, CA.
CEN/ISO. 2008. ISO 13790:2008: Energy Performance
of Buildings—Calculation of Energy Use for
Space Heating and Cooling, International
Organization for Standardization.
Cerezo, D. C., Cristoph, R. 2016. Urban Building
Energy Modeling—A Review of a Nascent
Field, Journal of Building and Environment.
Christensen, C., Anderson, R., Horowitz, S., Courtney,
A., and Spencer, J. 2006. BEopt™ Software
for Building Energy Optimization: Features
and Capabilities, Building America.
City and County of Denver. 2016. Denver Open Data
Catalog. http://data.denvergov.org/, April 7.
City and County of San Francisco. 2012. Building
Footprints (Zipped Shapefile Format),
Shapefile-Format-/jezr-5bxm, April 7, 2016.
City and County of San Francisco. 2016. DataSF,
https://datasf.org, April 7.
City of Portland. 2013. Building Footprints—Portland,
footprints-portland, April 7, 2016.
City of Portland. 2016. CivicApps for Greater Portland,
www.civicapps.org, April 7.
CoStar. 2016. www.costar.com, April 7.
Crawley, D.B., Lawrie, L. K., Pedersen, C.O., and
Winkelmann, F. C. 2000. EnergyPlus: Energy
Simulation Program, ASHRAE Journal.
Denver Regional Council of Governments Building
Outlines. 2014. http://data.denvergov.org/datas
outlines-2014, April 7, 2016.
EIA (U.S. Energy Information Administration). 2009.
Residential Energy Consumption Survey
EIA (U.S. Energy Information Administration). 2012.
Commercial Buildings Energy Consumption
Fleming, K., Long, N., and Swindler, A. 2012. The
Building Component Library: An Online
Repository to Facilitate Building Energy
Model Creation, 2012 ACEEE Summer Study
on Energy Efficiency in Buildings, Pacific
Grove, CA, pp. 94-106.
Gruchalla, K. 2004. Immersive Well-Path Editing:
Investigating the Added Value of Immersion,
Proceedings of IEEE Virtual Reality, pp. 157-
Hale, E., Macumber, D., Benne, K., and Goldwasser, D.
2012. Scripted Building Energy Modeling and
Analysis, SimBuild, Madison, WI, pp. 369-
Kolbe T. H., Gröger G., and Plümer L. 2005.
CityGML—Interoperable Access to 3D City
Models, Proceedings of the First International
Symposium on Geo-Information for Disaster
Management, Springer Verlag.
Krukowski, A., Majersik, C. 2013. Utilities' Guide to
Data Access for Building Benchmarking,
Institute for Market Transformation.
Krukowski, A. 2014. Creating Value from
Benchmarking: A Utility Perspective, Institute
for Market Transformation.
Long, N.L, Ball, B.L., Fleming, K. A., and Macumber,
D.L. 2014. Scaling Building Energy Modeling
Horizontally in the Cloud with OpenStudio,
Mercado A. C., Mitchell, R., Earni, S., Diamond, R.C.,
and Alschuler, E. 2014. Enabling
Interoperability through a Common Language
for Building Performance Data, 2014 ACEEE
Summer Study on Energy Efficiency in
Buildings, Pacific Grove, CA.
NASEO (National Association of State Energy
Officials) 2015. Board of Directors Resolution
on Access to Whole-Building Energy Data and
NIMA (National Imagery and Mapping Agency). 1997.
Technical Report TR8350.2: Department of
Defense World Geodetic System 1984, Its
Definition and Relationships With Local
Nouvel R., Zirak, M., Dastageeri, H., Coors, V., and
Eicker, U. 2014. Urban Energy Analysis Based
on 3D City Model, BauSIM, Fifth German-
Austrian IBPSA Conference.
NREL (National Renewable Energy Laboratory). 2014.
OpenStudio Measure Writers Reference Guide.
Polly, B., Kutscher, C., and Schott, M. 2016. From Zero
Energy Buildings to Zero Energy Districts.
2016 ACEEE Summer Study on Energy
Efficiency in Buildings, Pacific Grove, CA.
Reinhart, C., Dogan, T., Jakubiec, J.A., Rakha, T., and
Sang, A. 2013. UMI—An Urban Simulation
Environment for Building Energy Use,
Daylighting, and Walkability, Building
Simulation, Chambéry, France, IBPSA.
Robinson, D., Haldi, F., Kmpf, J., Leroux, P., Perez,
D., Rasheed, A., and Wilke, U. 2009. CitySim:
Comprehensive Micro-Simulation of Resource
Flows for Sustainable Urban Planning,
Building Simulation, Glasgow, Scotland,
Roth, A., Brooke, M., Hale, E.T., Ball, B.L., Fleming,
K., and Long, N. 2012. DEnCity: An Open
Multi-Purpose Building Energy Simulation
Database, 2012 ACEEE Summer Study on
Energy Efficiency in Buildings, Pacific Grove,
CA, pp. 251-262.
Roth, A., Goldwasser, D., and Parker, A. 2016. There's
a Measure for That!, Energy and Buildings,
Vol. 117, pp. 321–331.
SF Environment. 2016. Existing Commercial Buildings
Energy Performance Ordinance Report,
Energy-Performance-O/j2j3-acqj, April 7.
Larson, G.W., Shakespeare, R. 1998. Rendering with
Radiance, Morgan Kaufmann.
Wate, P., Saran, S. 2015. Implementation of CityGML
Energy Application Domain Extension (ADE)
for Integration of Urban Solar Potential
Indicators Using Object-Oriented Modelling
Approach, Geocarto International, Taylor and
Weaver, E., Long, N., Fleming, K., Schott, M., Benne,
K., and Hale, E. 2012. Rapid Application
Development with OpenStudio, 2012 ACEEE
Summer Study on Energy Efficiency in
Buildings, Pacific Grove, CA, pp. 307-321.