ufdcimages.uflib.ufl.edu · acknowledgments . first and foremost, i would like to thank my thesis...
TRANSCRIPT
GUIDELINES FOR USING BUILDING INFORMATION MODELING (BIM) FOR ENVIRONMENTAL ANALYSIS OF BUILDINGS
By
THOMAS J. REEVES
A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE IN BUILDING CONSTRUCTION
UNIVERSITY OF FLORIDA
2012
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© 2012 Thomas J. Reeves
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To my parents, Frances and Westley Reeves, and my brother, Lary Reeves
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ACKNOWLEDGMENTS
First and foremost, I would like to thank my thesis committee members, Dr.
Svetlana Olbina, Dr. Raymond Issa, and Dr. Ravi Srinivasan for their continued insight
and the direction that they brought to this research. Without their passion for research
and dedication to BIM and sustainability, this research could not have progressed to this
point. The rigor and knowledge they brought to this process was enormous and I am
truly grateful.
I would also like to thank the faculty of the Syracuse University School of
Architecture for putting my head in the clouds, and the faculty of the University of Florida
M.E. Rinker, Sr. School of Building Construction for putting my feet on the ground.
Finally, I must thank my family (from New Jersey to the Philippines) for their
continued and unwavering support in all of my endeavors. In particular I must thank my
mother, father, and brother, whose passion and dedication to their respective fields
continues to inspire me.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS.................................................................................................. 4
LIST OF TABLES............................................................................................................ 8
LIST OF FIGURES........................................................................................................ 10
ABSTRACT ................................................................................................................... 12
CHAPTER
1 INTRODUCTION .................................................................................................... 13
1.1 Problem Statement ........................................................................................... 14 1.2 Research Objectives......................................................................................... 14 1.3 Project Scope ................................................................................................... 16
2 LITERATURE REVIEW .......................................................................................... 18
2.1 Overview........................................................................................................... 18 2.2 BEM Applications.............................................................................................. 19
2.2.1 ASHRAE Standard 90.1 ......................................................................... 21 2.2.2 Use of BEM in Conceptual Design Phase .............................................. 22 2.2.3 Use of BEM in Design Development Phase ........................................... 22 2.2.4 Use of BEM in Construction Documents Phase ..................................... 23 2.2.5 Use of BEM in Construction and Contracting Phase .............................. 24 2.2.6 Use of BEM in Facilities Management Phase......................................... 25 2.2.7 Integrating BEM with BIM ....................................................................... 25
2.3 BEM Capabilities........................................................................................... 28 2.3.1 Inputs...................................................................................................... 29 2.3.2 Outputs................................................................................................... 31
2.4 Existing BEM Tools ....................................................................................... 34 2.4.1 EnergyPlus™ ......................................................................................... 34 2.4.2 eQuest™ ................................................................................................ 36 2.4.3 Autodesk Ecotect™................................................................................ 37 2.4.4 Autodesk Green Building Studio™ ......................................................... 38 2.4.5 Graphisoft EcoDesigner™...................................................................... 39 2.4.6 IES <Virtual Environment>™ (IES <VE>)............................................... 39 2.4.7 Bentley Hevacomp Simulator™.............................................................. 40 2.4.8 Bentley Tas Simulator™......................................................................... 41 2.4.9 DesignBuilder™ ..................................................................................... 42 2.4.10 Energy10™ .......................................................................................... 43 2.4.11 HEED™................................................................................................. 44 2.4.12 Visual DOE™ 4.0 .................................................................................. 44
2.5 Limitations of Building Energy Modeling ....................................................... 45
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3 RESEARCH METHODOLOGY............................................................................... 47
3.1 Initial Evaluation............................................................................................ 47 3.2 Case Study.................................................................................................... 48 3.3 Re-evaluation of BEM Tools Used in the Case Study ................................... 51 3.4 Developing Guidelines for BEM Selection and Application ........................... 52
4 RESULTS ............................................................................................................... 53
4.1 Initial Evaluation ............................................................................................ 53 4.1.1 User Friendliness.................................................................................... 54 4.1.2 Interoperability ........................................................................................ 56 4.1.3 Available Inputs ...................................................................................... 57 4.1.4 Available Outputs ................................................................................... 58 4.1.5 Cumulative Score ................................................................................... 59
4.2 Case Study.................................................................................................... 61 4.2.1 Energy Usage ........................................................................................ 62 4.2.2 Daylighting Performance........................................................................ 64 4.2.3 Natural Ventilation.................................................................................. 65
4.3 Re-Evaluation of Building Energy Modeling Tools Used in the Case Study ... 67 4.4 Guidelines for using Ecotect™, Green Building Studio™ and IES<VE>™...... 76
4.4.1 Model Preparation in Revit...................................................................... 77 4.4.2 Model Preparation in Building Energy Modeling Software....................... 77 4.4.3 Weather Data Acquisition....................................................................... 79 4.4.4 Schedule Implementation....................................................................... 80 4.4.6 Daylighting Analysis ............................................................................... 86 4.4.7 Natural Ventilation Analysis ................................................................... 88 4.4.8 Results Analysis in the Building Energy Modeling Tools ........................ 90
4.5 Guidelines for Using Building Energy Modeling............................................. 91 4.5.1 Guidelines for Building Energy Modeling Application ............................. 92 4.5.2 Guidelines for Building Energy Modeling Software Selection ................. 97
5 CONCLUSIONS AND RECOMMENDATIONS..................................................... 101
5.1 Conclusions................................................................................................. 101 5.1.1 Objective 1: Initial Evaluation ............................................................... 101 5.1.2 Objective 2: Case Study....................................................................... 101 5.1.3 Objective 3: Re-evaluation of BEM Tools Used in the Case Study....... 102 5.1.4 Objective 4: Developing Guidelines for Using Building Energy
Modeling...................................................................................................... 103 5.2 Research Limitations ................................................................................... 103
5.2.1 Objective 1: Initial Evaluation ............................................................... 103 5.2.2 Objective 2: Case Study....................................................................... 104 5.2.3 Objective 3: Re-evaluation of the BEM Tools Used in Case Study....... 106 5.2.4 Objective 4: Developing Guidelines for Using Building Energy
Modeling...................................................................................................... 106 5.3 Recommendations for Future Research ..................................................... 107
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APPENDIX
A INITIAL EVALUATION.......................................................................................... 109
B CASE STUDY....................................................................................................... 116
C GUIDELINES FOR USING BUILDING ENERGY MODELING ............................. 127
REFERENCES............................................................................................................ 131
BIOGRAPHICAL SKETCH.......................................................................................... 134
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LIST OF TABLES
Table page 3-1 Comparison of the buildings used in the case study........................................... 50
3-2 Profiles of rooms compared for daylighting analysis........................................... 51
4-1 Comparison of daylight factors for the selected rooms. ...................................... 65
4-2 Natural Ventilation Simulation Results for three BEM tools. Potential energy savings from natural ventilation (kWh) ................................................................ 66
4-3 Re-evaluation matrix with various weightings..................................................... 70
4-4 Re-evaluation of three BEM tools for interoperability .......................................... 72
4-5 Re-evaluation of three BEM tools for user friendliness ....................................... 73
4-6 Re-evaluation of three BEM tools for versatility. ................................................. 74
4-7 Re-evaluation of three BEM tools for speed........................................................ 76
4-8 BEM tool use during conceptual design phase................................................... 94
4-9 BEM tool use during design development phase................................................ 94
4-10 BEM tool use during construction documents phase.......................................... 95
4-11 BEM tool use during construction and contracting phase ................................... 95
4-12 BEM tool use during facilities management phase ............................................. 96
4-13 Recommended required inputs for BEM simulations in the different building lifecycle phases .................................................................................................. 98
A-1 lnteroperability subcriteria checklist and raw scores ...................................... 110
A-2 User friendliness sub-criteria checklist and raw scores .................................... 111
A-3 Available inputs subcriteria checklist and raw scores ....................................... 112
A-4 Available outputs checklist and raw scores ...................................................... 114
A-5 Cumulative score with respective criteria scores .............................................. 115
B-1 Annual Energy Usage Rinker Hall (output of Green Building Studio simulation) ........................................................................................................ 117
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B-2 Annual Energy Usage Gerson Hall (output of Green Building Studio simulation) ........................................................................................................ 118
B-3 Natural Ventilation Gains Rinker Hall (output of Ecotect simulation) ................. 120
B-4 Natural Ventilation Gains Gerson Hall (output of Ecotect simulation)................ 121
B-5 Natural Ventilation Potential Rinker Hall (output of Green Building Studio simulation) ........................................................................................................ 122
B-6 Natural Ventilation Potential Gerson Hall (Output of Green Building Studio simulation) ........................................................................................................ 122
C-1 Ecotect™ Guidelines and Recommendations Matrix .................................. 128
C-2 Green Building Studio™ Guidelines and Recommendations Matrix.............. 129
C-3 IES<VE>™ Guidelines and Recommendations Matrix .................................. 130
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LIST OF FIGURES
Figure page 2-1 Information exchange in building design and delivery workflows. A)
Traditional design/delivery. B) BIM-based collaboration. (Source: original)....... 26
2-2 Example of BEM data flow (adapted from US General Services Administration 2009)........................................................................................... 28
3-1 GbXML files of buildings used in the case study exported from Revit Architecture™. A) Rinker Hall. B) Gerson Hall ................................................... 49
4-1 Initial evaluation scoring system with criteria and subcriteria ............................. 54
4-2 User Friendliness................................................................................................ 55
4-3 Interoperability .................................................................................................... 57
4-4 Available Inputs .................................................................................................. 58
4-5 Available Outputs ............................................................................................... 59
4-6 Overall scores of the BEM tool initial evaluation ................................................. 60
4-7 The scores for available inputs and available outputs of the BEM tools.............. 61
4-8 Energy use intensity (EUI) comparison by building and by BEM tool. Dotted line denotes the CBECS national median EUI for educational building types (104 kBtu/SF) ..................................................................................................... 62
4-9 Energy use breakdown for two buildings used in case study using three BEM tools.................................................................................................................... 63
4-10 Diagram of building orientations relative to summertime prevailing winds........... 67
4-11 Re-evaluation scoring system with criteria and subcriteria.................................. 68
4-12 Re-evaluation un-weighted cumulative scores.................................................... 69
4-13 Location of weather data for three BEM tools in proximity to case study buildings ............................................................................................................. 79
4-14 Ecotect™ Schedule Editor .................................................................................. 81
4-15 Mean monthly average temperatures and corresponding comfort ranges. The shaded area refers to acceptable air-conditioned thermal comfort ranges, and the black lines refer to acceptable thermal range for natural ventilation. Dotted
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lines denote the acceptable thermal comfort range for given mean monthly outdoor temperatures (ASHRAE 2004)............................................................... 82
4-16 IES<VE>™ schedule editor interface.................................................................. 83
4-17 IES<VE>™ Modulating formula profile creation interface allows schedules to be derived from thermal parameters. .................................................................. 84
4-18 Green Building Studio™ run chart comparing buildings used in case study........ 85
4-19 Workflow of energy modeling methodology employed in case study .................. 91
4-20 Guidelines for BEM software selection ............................................................. 100
B-1 Rinker Hall energy use breakdown (output of Green Building Studio simulation) ........................................................................................................ 117
B-2 Rinker Hall annual fuel use breakdown (output of Green Building Studio simulation) ........................................................................................................ 118
B-3 Gerson Hall energy use breakdown (output of Green Building Studio simulation) ........................................................................................................ 119
B-4 Gerson Hall Energy Use Breakdown (output of Green Building Studio simulation) ........................................................................................................ 119
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Abstract Of Thesis Presented To The Graduate School Of The University Of Florida In Partial Fulfillment Of The
Requirements For The Degree Of Master Of Science In Building Construction
GUIDELINES FOR USING BUILDING INFORMATION MODELING (BIM) FOR
ENVIRONMENTAL ANALYSIS OF BUILDINGS
By
Thomas J. Reeves
August 2012 Chair: Svetlana Olbina Cochair: Raymond Issa Major: Building Construction
Building Information Modeling (BIM) efficiently integrates environmental analysis
into the design and delivery of high-performance buildings. Building Energy Modeling
(BEM), a subset of BIM, employs various simulation tools for predicting the
environmental performance of buildings. As the demand for high-performance buildings
has increased, BEM has facilitated the delivery of buildings that meet expected
performance requirements. The research objectives were to: 1) evaluate various BEM
tools, and 2) develop guidelines for using BEM tools in design and delivery of high-
performance buildings. Twelve BEM tools were evaluated using four criteria:
interoperability, user-friendliness, available inputs, and available outputs. The top three
programs were selected based on this evaluation and used in the case study to simulate
energy consumption, daylighting, and natural ventilation for two buildings, one LEED
certified and one non-LEED certified. The results of the case study were used to
compare the environmental performance of the two buildings and to develop guidelines
for using BEM tools to analyze building environmental performance.
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CHAPTER 1
INTRODUCTION
Building Information Modeling (BIM) efficiently integrates environmental analysis
into the design and delivery of high-performance buildings. Building Energy Modeling
(BEM), a subset of BIM, employs various simulation tools for analyzing the
environmental performance of buildings. As the demand for high-performance buildings
has increased, BEM has facilitated the delivery of buildings that meet expected
performance requirements. The development of such tools has been integral to the
process of integrated project delivery which tests and implements green building
strategies from design to execution. By integrating BEM with the specialties of various
other team members working around a centralized BIM model (e.g. structural,
mechanical, architectural, planning), the process has the potential to become seamless.
As sustainability becomes a standard practice in the building industry, the demand
for high-performance buildings increases. Goals related to sustainability are being set
ever higher, demanding greater levels of energy and resource efficiency (Bringezu,
2002). With the demand for high performance buildings and the resulting challenges
posed to designers and builders, the integration of building performance analyses into
the design and construction process becomes crucial. BIM in conjunction with BEM
seeks to make this integration seamless throughout the design process (US General
Services Administration 2005).
BEM allows design professionals to predict how well a building will perform upon
completion and provides greater insurance that designs will meet or exceed intended
performance requirements (Krygiel & Nies 2008). By allowing design professionals to
simulate building performance in a virtual environment, BEM tools provide feedback
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related to environmental responsiveness throughout the design process (Schlueter &
Thesseling 2009). The integration of BEM tools into design not only provides greater
certainty to designers and owners of a building’s performance, but also aids in the
design and construction of greener buildings. The use of BEM tools in the architecture,
engineering, and construction (AEC) industry has proven beneficial to both improve
building performance and to demonstrate energy efficiency to sustainability rating
systems like LEED.
1.1 Problem Statement
While the building sector comprises only 8% of the United States’ gross domestic
product, it is responsible for 40% of US energy consumption (US Department of Energy
2007) and 38% of carbon dioxide emissions (US Green Building Council 2007). The
development of building energy modeling and its integration into the design and
operation of the built environment could contribute to lowering these figures in one of the
most critical sectors for sustainability. Aside from the moral obligations related to
sustainability, the legal obligations of parties aiming to achieve a LEED certified building
make building energy modeling all the more necessary. There are currently several
existing BEM tools available for use in the AEC industry, and there is a need to
investigate and evaluate how these various tools can be employed.
1.2 Research Objectives
This research aimed to develop a set of guidelines and recommendations for using
building energy modeling for the analysis of high performance buildings. In particular,
the research focused on the building performance parameters of whole-building energy
use, daylighting, and natural ventilation potential. Intended users of the guidelines and
recommendations are building designers and green building consultants.
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The purpose of the research was to evaluate some of the most widely used BEM
tools in the US and to provide potential BEM users with recommendations in the
selection and utilization of a BEM tool. Different BEM tools are designed for different
applications and have varying learning curves, capabilities, and degrees of accuracy.
The research cross evaluated these BEM tools using a variety of criteria, and assessed
the application of the top three tools to aide potential BEM users in the selection and
integration of a BEM tool into building design and delivery.
There were four primary research objectives:
I. Initial evaluation of 12 BEM tools via literature review
II. Investigation of the top three BEM tools through a case study
III. Re-evaluation of the top three BEM tools used in the case study
IV. Developing a set of guidelines for using BEM for environmental analysis of buildings
The first project objective was to evaluate 12 major building energy modeling
(BEM) tools to identify the top three. In this stage the following BEM tools were
compared: Graphisoft EcoDesigner™, Bentley Tas Simulator V8i™, Bentley Hevacomp
Simulator V8i™, Autodesk Ecotect™, Autodesk Green Building Studio™,
DesignBuilder™, Visual DOE 4.0™, Energy10™, EnergyPlus™, E-Quest™ and
HEED™. The cross evaluation was then used to select the top three BEM tools based
on the identified criteria.
The top three BEM tools were selected to continue to the second phase of the
research and the second objective, which consisted of utilizing each simulation tool in a
case study. The case study was comprised of two comparisons. First the research
compared the analyses and simulations of the three programs for two buildings; one
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LEED certified (Rinker Hall) and one non-LEED certified (Gerson Hall). Secondly, the
case study also compared the results of each simulation for each of the three BEM tools
used. Each BEM tool is used to simulate each building’s performance in three areas of
building performance: energy usage, daylighting, and natural ventilation.
The third objective of the research was to select the strongest software based on
the criteria for evaluation. In this stage, a matrix was developed and used to re- evaluate
the software with various weightings assigned to the criteria for evaluation.
The fourth objective of the research was to develop guidelines for using BEM. The
guidelines were meant to help potential BEM users both in the selection of a BEM tool
and in BEM application. Guidelines were based on observations throughout the case
study’s energy modeling process and were organized by building lifecycle phase
application.
1.3 Project Scope
The overall aim of this research is to integrate BEM tools for environmental
analysis into the process of the design and construction of high-performance buildings.
In order to achieve this aim, guidelines and recommendations for the use and
application of BEM tools for the environmental analysis of buildings were developed. In
the first phase, the project focused on the evaluation of existing BEM tools. The three
most appropriate BEM tools were selected. The second phase consisted of the case
study. The BIM models for the two buildings (LEED certified and non-LEED certified)
were developed. Simulations of the environmental performance of these two buildings
were conducted using each of the three software identified in the first phase. Simulation
results in three categories (energy use, daylighting, and natural ventilation) were
analyzed and compared between the two buildings. In the third phase of the research,
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the most appropriate BEM tool was selected among the three used in phase two.
Guidelines for selecting and using BEM tools were then developed based on the
research findings.
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CHAPTER 2
LITERATURE REVIEW
2.1 Overview
Green building has become a standard practice in the construction industry in the
past 10 years. Aside from moral obligations to integrate energy efficiency into building
design and construction, numerous pieces of legislation at the federal, state, and local
levels have been passed in recent years providing either further incentives or mandates
to build green. Despite the transition of green building from fad to standard, it is still
difficult to predict whether or not a building as designed will perform at its desired level
upon completion. These uncertainties in regards to buildings performing at their
expected levels and the failures of many projects to meet these performance
requirements has led to many building owners forfeiting expected tax credits related to
green building. Lawsuits related to buildings failing to meet green performance
requirements have become common enough that these types of lawsuits have been
coined “LEED-igation” (Anderson et al. 2010).
To aid in the accuracy and predictability of green building performance, building
energy modeling (BEM) tools have been developed to simulate the environmental
consequences of building design. These tools aid design professionals in delivering
environmentally friendly buildings and provide greater insurance that buildings will
perform at their intended levels (Azhar & Brown 2009).
With green building becoming more of a standard practice in construction, the
integration of BEM tools into the design process becomes crucial. By allowing design
professionals to estimate and simulate building performance in a virtual environment,
BEM tools provide feedback related to environmental responsiveness throughout the
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design process. The integration of BEM tools into design not only provides greater
certainty to designers and owners of a building’s predicted performance, but also aids in
the design and construction of increasingly greener buildings (Krygiel and Nies 2008).
Building energy modeling can be applied in many phases of a building lifecycle.
While recent research suggests that the most important decisions related to building
sustainability occur during early design stages (Azhar and Brown, 2009), the potential
applications of BEM in facilities management (occupancy and operation phases) are
also being explored and implemented. BEM capabilities in terms of input and output
ranges are diverse as well.
As a research method, the literature review served not only as a basis for the
research but also as a means to develop the criteria to evaluate these tools. As such it is
comprised of two primary sections: BEM applications, and BEM capabilities. The BEM
Applications section investigates the use of BEM in various phases of the building
lifecycle and integration of BEM into various workflows. The BEM Capabilities section
investigates the range of inputs and outputs in existing BEM tools, and provides an
overview of 12 major BEM tools. The literature review concludes with a section
devoted to the limitations and future development of building energy modeling.
2.2 BEM Applications
BEM has proven useful during many phases of the building lifecycle. During the
pre-construction phase, BEM is used as an analysis tool to help inform green-minded
designers to devise greener design solutions. During the construction phase, BEM aids
contractors in acquiring building materials and components that meet performance
requirements. BEM integration into facilities management during the building operation
phase has also demonstrated positive results by testing potential system adjustments to
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increase energy efficiency of existing buildings. In this way, BEM can be integrated into
both facilities management and renovation and retrofit projects (US General Services
Administration 2009).
BEM tools are applied to the design and construction process of green buildings as
a design tool, and as a measurement tool. As a design tool, BEM may be integrated into
the early design phases when massing, orientation, and geometry are still being
developed. The performance of various conceptual models may be tested and adjusted
based on the feedback provided by BEM simulations. In an iterative design process,
building designers can rely on BEM to inform the development of building form towards
greener iterations (Krygiel & Nies 2008).
This type of BEM application is perhaps most efficiently employed when BEM is
used in conjunction with building information modeling (BIM) in which a central building
model is used throughout the design process. A building information model contains
numerous pieces of information related to building design and construction (e.g.
geometry, material properties, cost, etc.). As changes are made to the information
model, the environmental consequences can be tested in a BEM tool in a relatively
seamless way (Schlueter & Thesseling 2009).
At the other end of the design process when the building form is finalized and
designers are selecting materials and systems, BEM tools may be applied in more
detail-oriented ways related to design specifications. During later design stages or even
during building occupancy and facilities management, a BEM tool may be applied to
more accurately measure various loads, and to aid in adjusting design specifications
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(e.g. measuring the thermal performances of two different types of windows, and the
projected annual cost savings) (US General Services Administration 2009).
2.2.1 ASHRAE Standard 90.1
The implementation of BEM is outlined through the methodology in ASHRAE
Standard 90.1. This BEM process involves developing a benchmark model that uses
specified input values for certain building types and climate regions of the United States.
More energy efficient iterations of the model are then developed and compared against
the benchmark model to determine percent energy savings. This standard is the basis
for many green building assessment systems (e.g. LEED and Green Globes) that
include possible points towards certification related to building energy modeling and
energy simulation. ASHRAE 90.1 serves to provide industry standards for various
building types in various climatic regions to generate benchmark energy models
(ASHRAE 2011).
These standards provide the energy model with baseline inputs in regards to
occupancy schedules, lighting power densities and equipment power densities. The
benchmark model is used as the control to test various other design iterations against. In
this way, the success of a building design is measured as the percent of energy savings
against the benchmark model. For example, the LEED rating system uses this
methodology to assess optimization of energy performance for LEED Energy and
Atmosphere Credit 1 (EA Credit 1). An energy model that demonstrates that the building
will save 12% more energy than the baseline model is able gain one (1) LEED point. The
LEED EA credit 1 can provide up to 19 points if the energy model demonstrates 48% or
more energy savings (US Green Building Council 2011).
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More recent versions of the ASHRAE 90.1 standards for baseline energy models
are setting the bar at higher levels of energy efficiency making it difficult for designers
and energy modelers to develop designs that significantly outperform the baseline
model. This is indicative of the trend of sustainable development to set higher standards
for energy efficiency. With the bar for energy standards being set ever higher, the
integration of environmental analysis during the design process becomes more
necessary (ASHRAE 2007, 2010).
2.2.2 Use of BEM in Conceptual Design Phase
During the conceptual design phase BEM is integrated into making design
decisions related to massing, site selection and location, orientation, fenestration
strategies, and envelope using simplified and iterative building models (US General
Services Administration 2009). In this way, BEM can be used to quickly assess large-
scale ramifications of various designs, and compare these iterations in various
performance parameters. BEM informs building massing by providing feedback related
to solar exposure and prevailing winds exposure. Similarly, site selection, location of the
building within the site, and building orientation can also be informed by similar
environmental conditions. Based on local climate conditions, BEM can be used for
testing numerous building envelope constructions to try to minimize reliance on active
heating and cooling systems as well. Similarly, BEM may also be used to make
preliminary decisions about building systems during the conceptual design phase.
2.2.3 Use of BEM in Design Development Phase
During the design development phase, BEM aids in fine tuning decisions on
systems selections, building envelope, and glazing strategies. At this stage, the benefits
of BIM-based energy analysis become more evident. With geometry, site location, and
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orientation already established during the conceptual design phase, building energy
modelers may begin to work directly off of more detailed BIM model design iterations (as
opposed to re-creating the buildng geometry for every design iteration within the BEM
platform). Building energy modelers can isolate a number of variables to evaluate and
compare in more detailed analyses. Such variables may include glazing type (e.g. low-e,
double glazed), visible transmittance of glass, glazing U-value, envelope constructions
(with more detailed inputs for envelope layers and thermal properties), mechanical
equipment, and building controls. For example, energy models can compare the
daylighting benefits, energy savings, initial cost and lifecycle costs for two different
models of windows based on manufacturer specifications. As decisions become
finalized, BEM may also be used to estimate the actual energy performance of the
building upon completion (US General Services Administration 2009).
2.2.4 Use of BEM in Construction Documents Phase
During the construction documents phase, designers use BEM to finalize
estimations of building energy usage (US General Services Administration, 2009).
These estimations may be used to demonstrate the design’s code compliance and
ability to save certain levels of energy in relation to a baseline model (as outlined by the
methodology described by ASHRAE 90.1) in order to obtain sustainability assessment
credits (e.g. LEED EA Credit 1) (US Green Building Council 2007). With BEM aiding in
the selection of system manufacturers and suppliers, BEM material and building
component databases are also helpful to develop schedules and performance
requirements.
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2.2.5 Use of BEM in Construction and Contracting Phase
For contractors, BEM is especially useful for projects that must meet certain
performance requirements. During the construction phase, BEM is used to assess the
environmental impacts of change orders and to evaluate and compare the performances
of different components when selecting manufacturers, subcontractors, and material
suppliers (US General Services Administration 2009).
For example, a performance requirement may demand that the project obtain
LEED indoor environmental quality (IEQ) Credit 8.1. This credit is obtained if the project
is able to provide adequate daylight to at least 75% of regularly occupied spaces. This
may be obtained through the demonstration of computer simulation and a contractor
may quickly test the models of various window manufacturers to estimate whether the
system will meet IEQ 8.1 requirements.
BEM is also useful for contractors in material documentation during the
construction phase (Azhar et al. 2011). Material documentation is a necessity to obtain
up to 12 LEED credit points related to reusable / recyclable material selection (Materials
and Resources Credits), and non-toxic materials (Indoor Environmental Quality Credits).
A recent case study by Azhar et al. (2011) demonstrated how BEM became useful by
integrating into a Revit™-based BIM workflow for the purposes of material
documentation. The study exported the BIM model from Revit™ as a gbXML file and
imported it into the BEM software IES<VE>™. The report used the material takeoffs
generated in Revit™ to provide outputs of reports comparing the model with the
requirements for LEED credits.
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2.2.6 Use of BEM in Facilities Management Phase
The potential of BEM implementation into the facilities management and operation
phases of the building lifecycle are still being explored. The General Services
Administration (2009) describes one application of BEM in which the energy model is
calibrated with metered data from actual building operation. System levels can then be
adjusted in the virtual environment to identify errors in system operation and methods to
optimize system performance. A similar approach may be taken to retrofit analysis in
which a benchmark model is calibrated to simulate existing energy consumption, while
iterative energy models are tested to identify measures that can improve energy
efficiency.
The integration of BEM into a real-time data feed is the next step for sustainability
in facilities management. This has been demonstrated in an experiment by Clarke et al.
(2002) in which the BEM simulations provided real time adjustments based on a live
stream of measured data from the actual building. In this way, building systems may
continuously be optimized based on how the building is being used over time. Platt et al.
(2010) also demonstrated facilities managers can proactively optimize building energy
consumption with the aid of energy modeling. Like the Clarke et al.’s study, Platt et al.
used a real-time data feed from measured data from actual building operation. Based on
these inputs, an energy model was developed and calibrated with actual building
performance. The energy model integrated a genetic algorithm to optimize system levels
and reduce energy consumption.
2.2.7 Integrating BEM with BIM
One of the major benefits of using BIM as opposed to traditional design
methodologies, is that BIM allows for a team of experts from various fields to collaborate
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throughout the building lifecycle (Figure 2-1). In traditional design and delivery
methodologies, the work performed on the building design by architects, structural
engineers, MEP engineers, and contractors occurred in relative isolation to one another.
BIM allows for all of these fields to work collaboratively around a centralized building
information model. This is largely due to BIM being more than just 3D graphical
representations of a building design. BIM elements have the capacity to hold an array of
information related and useful to professionals from diverse areas of expertise. In this
way, BIM supports an integrative and collaborative approach to building design and
delivery (Eastman et al. 2008). Interoperability between BIM and other performance
analysis software such as many of the BEM software included in this study is also
improving to further support and streamline this collaborative environment.
A B Figure 2-1. Information exchange in building design and delivery workflows. A)
Traditional design/delivery. B) BIM-based collaboration. (Source: original).
BEM is a subset of BIM. In typical BIM-based work flows, energy modelers are part
of a larger BIM team along with specialists in the structural, MEP, architectural, and
construction professions. The interoperability of BEM with BIM platforms like Revit™ is
advantageous in that it allows building designers to test design decisions made within
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the BIM platform without having to recreate these changes in the BEM software. The
interoperability between BIM and BEM is still developing as errors in the translation
process are not uncommon (Schlueter & Thesseling 2009). Some BEM tools also
operate as plugins to existing BIM platforms such as Revit™ or ArchiCAD™ or 3D
modeling software like SketchUp™. In this way, design decisions made within the BIM
platform can occur with nearly seamless environmental feedback from the energy
model.
The two primary data schemas that allow BEM software to interoperate with other
BIM platforms are Green Builidng Extensible Markup Language (gbXML) and Industry
Foundation Classes (IFC) (Dong, et. al 2007). GbXML was developed to facilitate
interoperability between BIM platforms like Revit™ and energy analysis software (BEM).
GbXML allows objects created in the BIM platform to contain information pertinent to
green building performance such as thermal conductance, reflectivity, etc. This allows
for a streamlined exchange of information between 3D BIM modeling and performance
analysis (Dong et. al 2007).
The IFC data schema was developed by the Interanational Alliance for
Interoperability (IAI) in an effort to establish a standard and comprehensive data schema
for virtual environment architecture, engineering, and construction (AEC) industry
objects (e.g. doors, windows, walls, etc.). Rather than just being 3D graphical
representations of these objects, IFC objects are “smart objects” with various pieces of
information attached to them including material properties (Vanlande et al. 2008). IFC
information is object-based as opposed to geometry-based. Geometery is one of many
pieces of information attached to objects. In developing IFC, the IAI sought to create a
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non-proprietary data schema that could be a common file among the various trades in
the AEC industry. IFC can also be used during facilities management to facilitate
building operation (Khemlani 2007).
2.3 BEM Capabilities
As of 2011, the U.S. Department of Energy lists 374 building energy modeling
programs in its Building Energy Software Tools Directory (U.S. Department of Energy
2011). The range of capabilities between various existing BEM software is diverse.
Typical BEM software operate by entering a set of inputs that are run through a
simulation engine (Figure 2-2). The simulation engine provides a range of outputs
pertaining to building performance.
Different BEM tools come with different arrays of inputs and outputs. Some
software may have a narrow range of outputs and a deep set of required inputs. Such
software focuses on one (or a few) primary area(s) of building performance. Other
software may only require a small set of inputs to generate a wide range of outputs. Still,
other BEM tools exist that are comprehensive in both inputs and outputs (Krygiel & Nies
2008).
Figure 2-2. Example of BEM data flow (adapted from US General Services
Administration 2009)
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Whole building energy usage is affected by numerous factors. In theory, more
inputs and factors entered into the building energy model will increase the accuracy of
simulations. The following sections describe several of the typical inputs and outputs in
building energy modeling.
2.3.1 Inputs
Building Geometry: Building geometry refers to the form, dimensions and
orientation of a building. Included in the building geometry is the layout of rooms. This
input may also include information on openings (i.e. windows and doors) and their
locations (Krygiel & Nies 2008).
Building Location: Building location refers to the site of the building. The
specificity of building location differs between BEM tools. This may be input into a BEM
tool either as an exact address, global coordinates, zip code, city, or closest city to the
site of the building. This may even include an input for local terrain conditions such as
urban, forested, rural, etc. This input may sometimes be synonymous with climate and
weather data when BEM tools derive these inputs automatically based on the building
location (US Department of Energy 2011).
Envelope Constructions: Building envelope refers to walls, floors, and roof; i.e.
the building components that enclose space. The envelope construction input allows
users to specify materials and material properties for these building components. This
input plays a significant role in building thermal performance. Envelope constructions
should allow the user to specify thermal properties like R-value or U factor, and
reflectivity. This allows users to test different materials and simulate the potential
benefits to thermal efficiency (Sozer 2010).
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Occupancy Schedule: The occupancy schedule is derived from the expected
number of people inhabiting a building or room, and occupants’ presence throughout the
day, week, and year. Different thermal zones may be assigned individual occupancy
schedules. These values are typically input into the BEM tool as percentages of the
maximum occupancy load per zone. While this type of schedule is fixed, the
development of dynamic schedules has aided the integration of BEM tools into real-time
analysis for facilities management (Kwow & Lee 2009).
Operational Schedule: Operational schedules input the times at which building
systems are being used and to what capacity (typically by percentage). Operational
schedules may be assigned to such building systems as HVAC, lighting, and equipment
(IES<VE> 2011).
HVAC data: HVAC data includes the type of HVAC system intended to be used in
the building, its efficiency, design fan flows, heating capacities, cooling capacities, and
exhaust. This may also include estimated peak and off-peak times (Clark 2001).
Required Indoor Temperature: Required indoor temperature is the temperature
range to be maintained throughout the year, and is also referred to as thermal comfort
range. This may be expressed as heating and cooling set points, and further described
by a throttling range (the temperature threshold at which the HVAC is triggered on to
maintain the intended temperature). ASHRAE 90.1 sets standard thermal ranges that
must be maintained for occupant thermal comfort. Based on this input, some BEM tools
will provide outputs on how many hours throughout the year the building and HVAC
system is not able to meet the thermal comfort range. These are known as unmet hours.
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Unmet hours help BEM users identify times of the year when HVAC system levels must
be adjusted to meet thermal comfort requirements (ASHRAE 2011). Weather Data:
Weather data files express the climate of the building location (average
temperatures, solar exposures, etc. throughout the year). This is often downloaded from
a weather file database such as that provided by the US Department of Energy.
2.3.2 Outputs
Energy Usage: Energy usage is a calculation of energy used by a building at
specific time intervals – hourly, daily, monthly, and annually. Common units for energy
usage are watts, kilowatts, and kilowatt hours. Energy usage outputs may also included
an energy use breakdown showing what percentage of overall energy was used for
different functions, e.g. space heating, space cooling, lighting, equipment, pumps, and
fans (US Department of Energy 2011).
Carbon Emissions Calculations: Carbon emissions calculations allow users to
estimate the carbon footprint of the building, or how much carbon dioxide (CO2) a
building will emit over a specified period. This type of calculation is based on the amount
of energy consumed by a building and what type of energy it is consuming (often
assumed based on the building’s geographic location and typical energy sources for that
region). The carbon emissions calculation is measured by millions of metric tons (MMT)
of CO2 equivalent per kilowatt hour (US Department of Energy 2011).
Resource Management: In regards to building energy modeling, resource
management refers to an estimation of the available potentials for solar and wind
energy. Some tools allow users to create materials databases related to the types of
materials for construction, and allow users to estimate land use and energy impacts
related to material extraction and manufacturing (Azhar 2011).
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Thermal Analysis: Thermal analysis is derived from simulations of heat transfer
processes (i.e. convection, conduction, radiation) through the building and the building
envelope. Thermal analysis includes temperature profiles and comfort studies of thermal
zones (US Department of Energy 2011).
Heating / Cooling Load Calculations: Heating and cooling load calculations are
the amount of heat or heat removal over a given time to keep a building at a certain
temperature. ASHRAE and the Chartered Institution of Building Services Engineers
(CIBSE) calculation methods are the prominent models for heating and cooling loads.
Typical units are in mBtu and kWh (Clark 2001).
Airflow: Ventilation simulation may come in the form of natural, HVAC, and/or
mixed-mode. These simulations use computational fluid dynamics (CFD) to assess the
airflow in and around buildings and room objects. The common units for airflow
simulations are miles per hour (mph) for natural ventilation, and cubic feet per minute
(cfm) for HVAC simulations (Hensen 2003).
Natural Ventilation: These simulations may be used to assess passive thermal
gains from natural ventilation, or to estimate thermal losses due to infiltration (e.g.
opening of doors). This may be assessed as a percentage of heating/cooling hours lost
or gained due to natural ventilation, or as a factor of the amount of energy lost or gained.
Some BEM tools allow users to implement operable window schedules that may be
devised to simulate the optimized use of natural ventilation. Computational fluid dynamic
(CFD) simulations may also be performed in some BEM tools. This is particularly
important to estimate average airflow rates through spaces. CFD analysis is useful in
microclimate analysis, in which isolated thermal zones may be assessed and designed
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in such a way to maximize airflow to regularly occupied spaces within the zone. Some
analysis tools refer to natural ventilation as infiltration. Infiltration is a more general term
that refers to the entry of outdoor air into interior spaces. Infiltration can be both
beneficial and detrimental to reducing heating and cooling loads. In temperate months,
infiltration can help reduce cooling loads. However, in colder months, infiltration can
raise heating loads. Similarly in warmer months, infiltration can also raise cooling loads
(Hensen 2003).
Solar Analysis: Solar path, position, and radiation for every hour of the year are
typical solar analysis parameters. As it affects building energy modeling, solar analysis
measures the solar radiation on building surfaces and its effects on heat transfer.
Results from solar analysis may be used to inform designers about shading strategies,
arrangement, position, and orientation of photovoltaic arrays, and may be used to
estimate potential passive heating gains. Solar analysis is also an essential calculation
for other outputs like daylighting simulation and thermal analysis. Outputs may be visual,
graphical, and/or numerical (Reynolds & Stein 2000).
Daylighting Assessment: Daylighting assessment provides users with an
estimation of how much the building can rely on natural daylighting to illuminate spaces
and reduce the need of electrical lighting. Common outputs are daylight factor and
daylight autonomy. Daylight factor is the ratio of indoor illuminance to outdoor
illuminance at specified times, and at specified locations within spaces (Reynolds &
Stein 2000). These locations are defined by the placement of sensor points. Typically,
sensor points are placed in the middle of the room and at the height of a working surface
(Velds & Christoffersen 2001. Daylight autonomy is the percent of time that a building
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can rely on natural daylighting to light the spaces (Reynolds & Stein 2000). Daylight
Autonomy calculation is preferred because it is less susceptible to inconsistencies in
modeling methodology by taking data from various times of day throughout the year.
Lighting Design: Simulates the energy efficiency and quality of electrical lighting
in a building. This type of analysis may also estimate the annual energy consumption for
lighting as it relates to a corresponding lighting or occupancy schedule. Typical outputs
may be in units of kilowatt hours (kWh) (US Department of Energy 2011).
Lifecycle Cost: Lifecycle analysis measures building cost, and a range of lifecycle
costs such as capital, electricity and fuel costs, annual maintenance, repair costs, and
may sometimes take inflation into account (Younker 2003).
2.4 Existing BEM Tools
There are several building energy modeling tools available supporting a wide range
of learning curves and capabilities. A survey conducted by Attia et al. (2009) found the
top 10 BEM tools by use in the United States. These programs were EnergyPlus™,
EnergyPlus™ SketchUp Plugin, eQuest™, Autodesk Ecotect™, Autodesk Green
Building Studio™, IES<VE>™, Visual DOE4.0™, Design Builder™, Energy10™, and
HEED™. Of the 10 programs listed, the survey found Ecotect™ and eQuest™ to be the
most widely used. The following sections outline these 10 programs along with three
other major BEM tools: Bentley Hevacomp Simulator™, Bentley Tas Simulator™, and
Graphisoft EcoDesigner™.
2.4.1 EnergyPlus™
EnergyPlus™ is a module-based program that specializes in energy analysis and
thermal load calculation. While a number of graphical interfaces are available to be used
in conjunction with EnergyPlus™, as a standalone program its inputs and outputs are
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entirely text-based. Some of its notable capabilities include sub-hourly, user- defined
time steps for analysis and thermal load calculations that take transient, radiant,
conductive, and convective heat transfer, as well as moisture absorption/desorption into
account. Based on these conditions, EnergyPlus™ is able to accurately predict space
temperatures and the necessary heating, cooling, and electrical systems response to
maintain occupant comfort (Crawley et al. 2005). Some of EnergyPlus™’ other key
capabilities include advanced fenestration calculations that support variables of shading
devices, electrochromatic glazing, and number of other high performance commercially
available window types; advanced daylighting simulations that provide insight on both
interior illuminance levels and heat gains from artificial lighting; and atmospheric
pollution calculations providing estimates on CO2, SOx, NOx, CO, particulate matter,
and hydrocarbon production from building and energy conversion activities both on and
off site (US Department of Energy).
Features / Capabilities of EnergyPlus™ are:
• Sub-hourly, user-defined time steps
• Atmospheric pollution calculations
• Transient heat transfer (conduction, convection, radiation) calculations included in thermal loads calculations
• Advanced glazing inputs – e.g. controllable window blinds, and electrochromic glazing
• Extensive material and component library including several commercially available products
• Sketchup Plugin
Advantages of EnergyPlus™ are:
• Rigorous and in-depth calculations • Widely used energy analysis software
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• Common calculation engine for other BEM tools • Free to download
Disadvantages of EnergyPlus™ are:
• Inputs and outputs are entirely text-based (no graphical interface) • Not very “user-friendly” • Limited range of outputs (Smith et al., 2011). 2.4.2 eQuest™
Developed by the Department of Energy (DOE), eQuest™ (“the Quick Energy
Simulation Tool”) is a free and comprehensive building energy simulation program. It
includes a graphical interface and building creation wizard to guide users through the
basic building inputs. The energy efficiency measure (EEM) wizard allows user to
include more detailed and performance-based inputs to compare the results of various
design alternatives (US Department of Energy, 2011). It uses the latest DOE-2
simulation engine and provides extensive and detailed results in its simulation reports
that can be compared side by side with simulations using different combinations of
energy efficiency measures. The report is broken down into hourly time steps over the
entire year (Crawley et al. 2005).
Features / Capabilities of eQuest™ are:
• Uses DOE 2.2 building energy analysis software as its calculation engine • Wizard-based inputs • Detailed analysis reporting broken down by hourly time-steps and on a zonal basis
Advantages of eQuest™ are:
• Supports simple to detailed models • Quick calculation time • Validated by US Department of Energy and ASHRAE • Provides a wide range of outputs • Free to download
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Disadvantages of eQuest™ are:
• Limited and simplified infiltration / natural ventilation simulations • 3-D model geometry is built in the software (can not be imported) • Not very user-friendly outside of wizard-based inputs • Does not simulate interior glazing in daylighting calculations • Sensitive to model errors 2.4.3 Autodesk Ecotect™
Ecotect™ is a comprehensive energy analysis software with a focus on graphical
output. Analyses types supported by Ecotect™ include (but are not limited to) thermal,
solar, lighting analysis and acoustic analysis (US Department of Energy). Ecotect™’s
most notable feature is its robust and interactive graphical outputs. Each analysis type
can be represented in a number of different graphs or with a versatile analysis grid that
can be mapped over any surface of the model. Ecotect™’s graphical outputs may be
saved and exported as bitmaps, metafiles, and in some cases as animations. Ecotect™
is intended to be an early design phase tool. Ecotect™’s developer, Autodesk argues
that the most critical and effective decisions pertaining to green design are made in the
conceptual design phase. Ecotect™ is tailored to this idea by being able to provide
visual and analytic feedback to extremely simple sketch models (Crawley et al. 2005).
Features / Capabilities of Ecotect™ are:
• IFC and gbXML import • Analysis grid • Dynamic graphical outputs – animations • Solar, thermal, lighting and acoustics analysis • Lifecycle analysis
Advantages of Ecotect™ are:
• Building geometry editing • Scalable graphical analyses
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• Online Autodesk user support (AUGI Forums)
Disadvantages of Ecotect™ are:
• long calcuation times • sensitivity to modeling errors • user interface is not user friendly (Azhar, 2009) 2.4.4 Autodesk Green Building Studio™
Green Building Studio™ is a web-based BEM tool. As such it does not include its
own 3D modeler and must rely on a gbXML-enabled or IFC-enabled BIM or 3D
modeling platform for the creation of building geometry. Upon importing building
geometry, Green Building Studio™ guides the user through a baseline simulation
providing a report detailing estimated CO2 emissions, energy analysis, potential for
natural ventilation, lifecycle cost and other analyses. Alternative simulations using
various combinations of energy efficiency measures may then be run and compared to
the baseline and each other (US Department of Energy, 2011). Another aspect of Green
Building Studio™ is that as a web-based energy analysis program, simulations are run
through the internet as opposed to the user’s microcomputer. This allows for simulations
to be performed much quicker than with most other computer-powered simulation
programs (Azhar, 2009).
Features / Capabilities of Green Building Studio™ are:
• Energy usage, carbon emissions, daylighting, ventilation • Lifecycle assessment • Online interface • Alternative run comparisons
Advantages of Green Building Studio™ are:
• Interoperability with Revit • Fast calculation times • Requires minimal preparation to run the base simulation
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Disadvantages of Green Building Studio are™:
• Unable to customize outputs • Relies on third party software to model building geometry 2.4.5 Graphisoft EcoDesigner™
EcoDesigner™ allows for immediate feedback pertaining to environmental
performance during early design stages. It is a tool that is integrated into the Graphisoft
ArchiCAD™ BIM platform. As such it allows energy analysis to be performed very
quickly while designing in ArchiCAD™. In addition to building geometry in ArchiCAD,
EcoDesigner™ provides inputs for HVAC, location, and thermal properties of building
envelope elements (Thoo, 2008).
Features / Capabilities of EcoDesigner™ are:
• Strusoft Corecalculation engine (ASHRAE 90.1 compliant) • ArchicCAD™ plugin • Calculates energy consumption, carbon footprint, monthly energy breakdown
Advantages of EcoDesigner™ are:
• Interoperability as a plugin to ArchiCAD™ • User-friendly interface • Quick calculation types
Disadvantages of EcoDesigner™ are:
• Provides minimal opportunity for customization • Relies on default values for many calculations • Limited options for inputs and outputs 2.4.6 IES <Virtual Environment>™ (IES <VE>)
IES <VE>™ is a comprehensive BEM software that uses a set of modules to
perform various calculations and simulations. These modules are all linked together by a
common user interface and a single integrated data model. Modules included in the IES
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<VE>™ package include ModelIT for building geometry creation, ApacheCalc for load
analysis, ApacheSim for thermal analysis, MacroFlo for natural ventilation analysis,
ApacheHVAC (HVAC simulation using components), SunCast for shading visualization,
MicroFlo for three-dimensional computational fluid dynamic calculations,
FlucsPro/Radiance for daylighting analysis, DEFT for model optimization, LifeCycle for
life-cycle cost and energy analysis, and Simulex for building egress simulations (Crawley
et al. 2008).
Features / Capabilities of IES<VE>™ are:
• Outputs include energy usage, carbon emissions, thermal analysis, ventilation and airflow, solar analysis, daylighting, lifecycle analysis
• Building geometry editing and modeling
• Analysis grid
• gbXML model error check
Advantages of IES<VE>™ are:
• Comprehensive building performance tool • User-friendly interface • Includes direct plugin to Revit to improve interoperability
Disadvantages of IES<VE>™ are:
• Analysis results are saved in different folders • gbXML model error check is required (Azhar, 2009) 2.4.7 Bentley Hevacomp Simulator™
Hevacomp Simulator™ uses EnergyPlus™ as its simulation engine. As such it
creates a connection between BIM platforms like Bentley and Revit and uses those as a
graphical interface for EnergyPlus analyses. Hevacomp™ also provides compliance
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services to support UK Part L and ASHRAE 90.1 compliant buildings (Bentley Systems,
2011).
Features / Capabilities of Hevacomp Simulator™ are:
• Building energy standard compliance tools for CIBSE, ASHRAE, ISO, and LEED
• EnergyPlus calculation engine
• Calculations include energy usage, natural and mechanical ventilation, airflow, thermal analysis, and renewable energy potential (solar and wind)
• gbXML enabled
Advantages of Hevacomp Simulator™ are:
• Interoperability with other Bentley-based BIM software • Compliance with building energy standards and certification • Detailed and accurate analysis • Predefined and user-defined HVAC systems
Disadvantages of Hevacomp Simulator™ are:
• Outputs are limited to energy and thermal analysis • Requires some expertise in MEP • Limited user support 2.4.8 Bentley Tas Simulator™
Tas™’s primary function is thermal analysis. Thermal simulations provide the basis
for other analyses including energy consumption, energy operating costs, lifecycle costs,
CO2 emissions, and occupant comfort. Tas also provides features allowing for the
simulation of passive design strategies like natural ventilation. Another feature included
in Tas™ is a compliance check that allows the user to ensure that the design is
compliant with major green standards like ASHRAE 90.1 LEED credit, UK regulations
Part L2 and EP certification (Bentley Systems, 2011).
Features / Capabilities of Tas Simulator™ are:
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• gbXML import
• Outputs include thermal analysis, natural ventilation and airflow, energy use, CO2 emissions, occupancy comfort, and component sizing
• Compliance with ASHRAE 90.1, LEED, and CIBSE
Advantages of Tas Simulator™ are:
• Positive feedback from users on gbXML import
• Provides feedback for component sizing
• Includes a Facilities Management Tool to model changes to systems while in operation
Disadvantages of Tas Simulator™ are:
• Tailored towards detailed analysis • Requires some MEP expertise • Limited user support 2.4.9 DesignBuilder™
DesignBuilder™ was developed to be an easy-to-use BEM software. It is best
suited for early design stage modeling in which the user can quickly evaluate various
design options for energy consumption and environmental comfort with the option of
including detailed analysis for potential natural ventilation (US Department of Energy,
2011).
Features / Capabilities of DesignBuilder™ are:
• Outputs include energy usage, CO2 emissions, solar shading, daylighting, natural ventilation and airflow, and thermal analysis
• Calculates heat transmission (conduction, convection, radiation).
• EnergyPlus calculation engine
Advantages of DesignBuilder™ are:
• Building geometry can be altered • Natural ventilation simulations require minimal preparation work
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• User-friendly
Disadvantages of DesignBuilder™ are:
• Limited HVAC inputs • Limited interoperability with 3D/BIM platforms 2.4.10 Energy10™
The major strength of Energy10™ is its automatic output of more-efficient design
alternatives based on the initial baseline simulation. Design alternatives use a number of
predefined strategies altering building envelope and building systems (HVAC, lighting,
daylighting, and photovoltaic potential). A limitation of Energy10™ is that it only
analyzes one or two thermal zones at a time. As such it is better suited for the analysis
of smaller projects (10,000 square feet or less). Energy10™ also includes a lifecycle
cost analysis tool (Crawley et al. 2008).
Features / Capabilities of Energy10™ are:
• Energy, thermal, and daylighting simulations • Hourly time-steps for calculations over entire year • Comparison of alternative designs • ASHRAELIB – ASHRAE compliant building components library
Advantages of Energy10™ are:
• Requires minimal inputs to run baseline simulation • Calculation speed is fast • Default values are adjustable
Disadvantages of Energy10™ are:
• Limited to building models of one or two thermal zones, and floor area under 10,000 SF.
• Limited HVAC inputs
• Requires some programming knowledge
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2.4.11 HEED™
HEED™ is a free, user-friendly, single zone energy simulation program developed
by UCLA. Its interface is largely wizard based, and 3d modeler is exceptionally easy to
use. Relying only on floor area, number of stories, location, and building type as inputs,
HEED™ generates two design iterations with one being 30% more energy efficient than
the other. HEED™ can manage up to 9 iterations for 25 projects.
Features / Capabilities of HEED™ are:
• Passive design inputs – thermal mass, night ventilation, high-performance glazing • Simulates energy usage, CO2 emissions, lifecycle cost
Advantages of HEED™ are:
• User-friendly • Provides detailed inputs • Automatically generates design alternatives
Disadvantages of HEED™ are:
• Limited to four thermal zones • Limited HVAC options • Weather data is limited to California 2.4.12 Visual DOE™ 4.0
Visual DOE4.0 is a windows interface for the DOE2.1 building energy calculation
engine. Users create the building geometry in Visual DOE™ by importing a DXF file of
the floor plan from a CAD software and filling in the spaces using blocks in the model
editor. Users can specify bulidng envelope constructions and HVAC system types from
the libraries. Visual DOE™ also features a design alternative generator that can provide
up to 99 different design iterations for building envelope and HVAC.
Features / Capabilities of Visual DOE™ 4.0 are:
• Thermal and energy analysis
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• DOE 2.1E calculation engine • Design alternative generator • Hourly time step results
Advantages of Visual DOE™ 4.0 are:
• Users can compare several alternatives very quickly • Requires minimal inputs to run base simulation • Useful as schematic design tool
Disadvantages of Visual DOE™ 4.0 are:
• Building geometry must be recreated in the software • Limited user support • Limited outputs
2.5 Limitations of Building Energy Modeling
Although building energy modeling presents designers, builders, and building
owners with an array of powerful tools to assess and predict building performance, many
of these programs are yet to be validated. It should be noted that these tools provide
only estimates (some much rougher than others). While the implementation of many
inputs allows for accurate models, building energy is affected by many factors that
cannot be predicted. Climate data is based on averages for various locations, and differs
from year to year. Building occupancy may be simulated by an occupancy schedule,
however it is impossible to predict the actual behavior of occupants during building
operation. The variability in how building occupancy actually occurs is a common
source for energy model errors. Because of this variability, accuracy of predicting how
a building will perform upon completion is compromised.
Building energy simulation is tapping into the potential of integrating real-time data
feed into the calibration process, however these developments are still in their early
stages. Such technology aids in both increasing the accuracy of energy modeling, and
45
improving energy efficiency by using energy simulation to aid in the optimization of
building performance based on actual tendencies in building operation.
As a design tool, BEM pushes architects and engineers towards an integrated
design approach. Interoperability between BEM and BIM and other 3D modeling
applications is supported by many programs. However, it is not uncommon for errors in
the building model to arise in the translation process between BIM to BEM (Azhar &
Brown 2009). There is still much potential to push interoperability further to make design
and environmental analysis an even more seamless process (Thoo 2008).
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CHAPTER 3
RESEARCH METHODOLOGY
The research methodology was broken down into three parts based on the three
objectives. Section 3.1 describes the research methodology to conduct a cross
evaluation of 12 major BEM tools. Section 3.2 provides the methodology of the case
study; and section 3.3 describes the methodology for the re-evaluation and development
of guidelines for using BEM for the environmental analysis of high performance
buildings.
3.1 Initial Evaluation
Twelve major BEM tools were selected for the initial evaluation. These programs
were Graphisoft EcoDesigner™, Bentley Tas Simulator V8i™, Bentley Hevacomp
Simulator V8i™, Autodesk Ecotect™, Autodesk Green Building Studio™, IES <VE>™,
DesignBuilder™, Visual DOE 4.0™, Energy10™, EnergyPlus™, E-Quest™ and
HEED™. These BEM tools were selected based on a survey study by Attia et al. (2009)
to identify the most widely used energy simulation software in the United States. The
research included Bentley Tas Simulator V8i™ and Bentley Hevacomp Simulator V8i in
addition to the top 10 BEM tools identified in the Attia et al. survey, as these are
prevalent BEM tools used in the United Kingdom. The initial evaluation assesses these
programs using four main criteria: interoperability, user-friendliness, available inputs,
and available outputs. Within each criterion were a number of sub-criteria that were used
as a checklist for each criterion. The sub-criteria were identified based on the literature
review. Previous studies that compared the capabilities of existing BEM tools were
synthesized into the sub-criteria checklists. These studies included Crawley et al.
(2008), Attia et al. (2010) Azhar et al. (2009) and Azhar et al. (2011), as well as general
47
information provided for several BEM tools in the US Department of Energy’s Building
Energy Software Tools Directory (2011). The Crawley et al.’s study (2008) was
particularly instrumental in developing the sub-criteria checklist for available inputs and
available outputs. Azhar et al.’s study (2009), along with other BIM-oriented studies
including Thoo’s (2008), and Eastman et al.’s (2008) were used to develop the sub-
criteria checklist for interoperability. Azhar et al.’s study(2011) and Attia et al.’s study
(2010) were primary resources in developing the sub-criteria checklist for user-
friendliness. The scoring system placed an even weight of 1 point maximum for each
criterion. Per criterion, the BEM tool received a score between 0 and 1 based on the
percentage of sub-criteria supported by the software. Each BEM tool was scored using
this system to determine the top three programs of the 12 used in this portion of the
study.
3.2 Case Study
The top three BEM programs identified by the initial evaluation were used to
conduct a case study comparing the performance of two buildings. These buildings
(both on the University of Florida campus in Gainesville, Florida) were Rinker Hall (a
LEED gold certified building) and Gerson Hall (a non-LEED certified building). BIM
models were prepared for each building using Revit Architecture™ 2012. Each model
was exported as a gbXML file from Revit™ (Figure 3-1) and imported into each of the
three programs. The models were exported as “simple with shading” selected in the
gbXML export window in Revit™ to specify the level of detail.
48
Figure 3-1. GbXML files of buildings used in the case study exported from Revit
Architecture™. A) Rinker Hall. B) Gerson Hall
Specifications pertinent to each building (Table 3-1) were input into each BEM tool
(or to the best of the software’s capability). Each BEM program was used to simulate
both buildings’ performance in energy usage, daylighting, and natural ventilation. The
ability to input these specifications was different between BEM tools. Some software like
Green Building Studio™, allow for building constructions to be selected from a dropdown
menu, but do not provide the user with the ability to specify construction layers and
respective thermal properties.
The case study used energy use intensity (kBtu/SF) to compare the two buildings’
energy performance. Energy use intensity (EUI) was derived from the overall annual
energy usage divided by the building’s floor area. EUI was used as the unit to compare
the two buildings’ performances so as to remove any difference in energy usage based
on the difference in the two buildings’ square footages. A larger building with a greater
area of conditioned space is more likely to consume more energy than a building with a
smaller area of conditioned space.
49
Table 3-1. Comparison of the buildings used in the case study Building Characteristics
Rinker Hall Gerson Hall
Date of completion 2003 2004 Location Gainesville, FL Gainesville, FL Area of conditioned space (sq. ft.)
42,719 38,632
HVAC system Variable Air Volume with Energy Recovery Ventilation
Variable Air Volume with Terminal Reheat
Building envelope construction (from exterior to interior)
¾” metal panel, 5.5” R20 cellulose insulation, 2” rigid insulation, ½” gypsum board
4” brick veneer, 2” air gap / damproofing, 12” CMU, 5/8” GWB on 1-1/2” studs with rigidinsulation
Exterior wall U- Value 0.033 0.097 Glazing type Low-E, double-glazed,
insulated Low-E, double-glazed
Glazing U-Value 0.53 0.66 Window to Wall Area Ratio
0.22 0.20
Albedo (Roof Reflectance)
0.80 0.41
To compare the daylighting performance, four rooms from each building were
selected (Table 3-2). The study compared similar rooms (similarities based on
orientation, room area, and room function) between the two buildings for each of the
three programs using daylight factor as the common unit. Ideally the study would have
compared daylighting based on daylight autonomy, but could not due to limitations of the
software. Because the two building have different orientations (the long axis of Rinker
Hall is oriented east to west while the long axis of Gerson Hall is oriented north to south),
the similarities between glazing orientations for room comparisons were limited. For
instance, the rooms selected for comparison for the office room type and graduate
studio room type had inconsistent glazing orientations because no such rooms exist in
the two buildings that have the same room function and glazing orientation. The rooms
selected were the closest fits of the rooms available for analysis.
50
Table 3-2. Profiles of rooms compared for daylighting analysis Rinker Hall Gerson Hall Room Designation
Area (sq. ft.)
Glazing Orientation
Room Designation
Area (sq. ft.)
Glazing Orientation
303 Main Conference
589
North
327 Large Conference
768
North
322 Faculty Office
139
West
324 Office
146
North
240 Est./Dwg./Sch.
1334
East
122 Medium Classroom
1162
East
340 CCE 527 East 329 PhD Office
274 North
The case study also assessed the natural ventilation and potential energy savings
of the two buildings using each of the three BEM tools. In particular, the research sought
to estimate the potential energy savings due to reliance on natural ventilation (i.e.
operation of operable windows). For natural ventilation analysis, the research assumed
optimized use of operable windows for both buildings. This meant that operable windows
were open at all moments of the year when outdoor climatic conditions would benefit
energy efficiency by reducing cooling loads. Again, different BEM tools provided for
different modeling methodologies, so the comparison was limited by the types of outputs
provided by the three software used in the case study.
3.3 Re-evaluation of BEM Tools Used in the Case Study
Upon completing the case study, a re-evaluation of the top three BEM tools was
conducted using a similar set of criteria as the initial cross evaluation. Adjustments and
additions were applied to the criteria and subcriteria based on information gathered
during the case study. The four criteria used in the re-evaluation were interoperability,
user-friendliness, versatility (of inputs and outputs), and calculation speed with updated
subcriteria for each criterion.
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First, the scoring system was used to select the best program based on an even
weight applied to each of the four criteria. A matrix was then developed applying
different weights to criteria based on order of importance for the potential user. In this
way, a potential BEM user could use the matrix by first identifying the order of
importance of the four criteria, and then be directed to the BEM tool most suitable to
their preference.
3.4 Developing Guidelines for BEM Selection and Application
Guidelines were organized by assessing the applicability of BEM to various
building lifecycle phases. These building lifecycle phases were conceptual design,
design development, construction documents, construction and contracting, and
facilities management. Guidelines were developed based on the use of each BEM
program during the case study. During the case study, the energy modeling
methodology for each BEM tool was investigated. The steps in the modeling process
under investigation were the following:
• model preparation in BIM (Revit Architecture) • model preparation in BEM • weather data acquisition • schedule implementation • energy analysis • daylighting analysis • natural ventilation analysis • results analysis
A log was maintained for each step documenting complications, advantages,
disadvantages, observations and the locations of help files / user manuals / tutorial
sources that were used for guidance during the energy modeling process. The
spreadsheets for each BEM tool are available in Appendix C.
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CHAPTER 4 RESULTS
The results related to the four major objectives of the research are presented in the
following sections. The initial evaluation identified IES<VE>™, Ecotect™, and Green
Building Studio™ to be the most appropriate BEM tools out of the 12 evaluated. These
three BEM tools were used in the case study to compare the energy usage, daylighting
performance, and natural ventilation potential of Rinker Hall (a LEED gold- certified
building), and Gerson Hall (a non-LEED certified building). Overall, the results showed
that Rinker Hall performed better than Gerson Hall in regard to energy usage and
daylighting performance (for the rooms selected), while Gerson Hall performed better
than Rinker Hall in natural ventilation potential.
4.1 Initial Evaluation
The initial evaluation used the scoring system illustrated in Figure 4-1. The
comprehensive score for each BEM tool was calculated as the sum of the individual
criterion scores. The four criteria were interoperability, user friendliness, available inputs
and available outputs. Each criterion was scored as the fraction of subcriteria supported
by each BEM tool over the total number of subcriteria evaluated for each criterion. The
criterion interoperability had five total subcriteria in the checklist. Thus the criterion score
for interoperability was calculated as x/5, where x denotes number of subcriteria
supported by the BEM tool. User friendliness had eight subcriteria and was calculated as
x/8. Available inputs had 25 subcriteria and was calculated as x/25, and available
outputs had 20 subcriteria and was calculated as x/20.
53
Figure 4-1. Initial evaluation scoring system with criteria and subcriteria
Of the 12 BEM tools investigated in the initial evaluation, Ecotect™, Green Building
Studio™, and IES<VE>™ scored the highest and were selected for use in the case
study. The following sections illustrate the breakdown of the initial evaluation based on
the four criteria (user friendliness, interoperability, required inputs, and versatility). Each
section shows the breakdown of subcriteria that went into each BEM tool’s score.
4.1.1 User Friendliness
The results from the analysis showed that the most user friendly software of the 12
BEM tools evaluated, were Energy10™, Green Building Studio™, and HEED™ received
the highest scores for user friendliness (Figure 4-2). Each of these BEM tools provided
for six out of the eight sub-criteria included in the User Friendliness checklist.
Ecotect™, DesignBuilder™, Visual DOE4.0™ and IES<VE>™ each provided for
five out of the eight sub-criteria. EQuest™, EcoDesigner™, Tas™, Hevacomp™, and
EnergyPlus™ scored the lowest out of the 12 BEM tools providing for four out of the
eight subcriteria in the User Friendliness checklist.
54
Figure 4-2. User Friendliness
Energy10™, Green Building Studio™, and HEED™ had the highest scores in the
user friendliness evaluation. These software provide users with extensive sources of
user-help and require minimal expertise to get base run results. One of Energy10™’s
major strengths as a user-friendly BEM tool is its capability to automatically provide the
user with more energy-efficient design alternatives. Users also provide very few inputs in
order to run a base simulation. Since Energy10™ is only intended for one-zone and two-
zone analysis, the modeling process is extremely simplified which is beneficial for users
with limited experience in 3D modeling. Similarly, HEED™ relies on very few inputs to
generate energy results. Although the program is extremely simple and intuitive to use,
many of its default settings and weather files are tailored to California, which can
complicate the modeling process for projects in other climatic regions. Green Building
Studio™ relies on third-party software (like Revit™) for the creation of building geometry.
When the BIM model is exported as a gbXML file to Green Buildling Studio™, the
process is not unlike HEED™ and Energy10™. Users fill out a quick questionnaire to
specify building type and location before the initial simulation can run. As Green Building
55
Studio™ is an Autodesk product, users also benefit from an extensive set of tutorials
and user-forums for user-friendliness. EQuest™, EcoDesigner™, and EnergyPlus™
received low scores in this criterion (4 out of 7). These BEM tools had did not have
simple user interfaces, had limited potential for customization, and did not provide
feedback related to more environmentally friendly design alternatives. The Bentley BEM
tools (Tas™ and Hevacomp Simulator™) also received low scores as these programs
are tailored to complex yet specialized analyses, and are intended for use by qualified
architects and engineers.
4.1.2 Interoperability
IES <VE>™ scored highest (five out of five possible points) for interoperability. IES
<VE>™ has capability of sharing information with each of the software / file types
evaluated. These included interoperability with gbXML file types and Google
SketchUp™. EcoDesigner™, Tas™, Green Building Studio™, and Hevacomp™
provided interoperability with all but SketchUp™ and scored four out of five.
DesignBuilder™ and Visual DOE 4.0™ allow DXF import to aid in the creation of
building geometry, but 3D models must be developed in each program’s “in-house”
model builder. HEED™ and Energy10™ demonstrated the lowest degree of
interoperability with none of the programs or file types being supported by import or
export capability. For these two programs, building geometry must be created within the
BEM tool. The results for interoperability evaluation are illustrated in Figure 4-3.
56
Figure 4-3. Interoperability
4.1.3 Available Inputs
IES<VE>™ had the highest score (20 out of 25) based on the available inputs
evaluation, followed by Ecotect™ (19 out of 25) and eQuest™ (18 out of 25). HEED™
(score six out of 25), Energy10™ (eight out of 25), Tas™ and Hevacomp™ (both scored
nine out of 25) had the lowest scores regarding available inputs. The top three BEM
tools in available inputs (IES<VE>™, Ecotect™, and eQuest™) may be considered the
more versatile software. Users can input values for a wider range of variables into the
model. While certain inputs were relatively constant for most of the software (building
geometry, location, material properties), the inputs that set IES<VE>™, Ecotect™, and
eQuest™ apart were more detail oriented. IES<VE>™ for example provides inputs for
MEP models with HVAC component sizing, and plant data. IES<VE>™ and Ecotect™
both have lighting system inputs that provide users with the ability to design and
simulate the effectiveness of electrical lighting. IES<VE>™, Ecotect™, and eQuest™ all
have inputs for required internal temperature, type of energy used, occupancy and
57
building function. The results from the available inputs evaluation are illustrated in Figure
4-4.
Figure 4-4. Available Inputs
4.1.4 Available Outputs
Green Building Studio™ (score 19 out of 20 possible), Ecotect™ (18 out of 20),
and IES<VE>™ (19 out of 20) had the most outputs of those included in the available
outputs evaluation. The software that received the lowest scores in this category were
EcoDesigner™ (6 out of 20), Visual DOE4.0™ (6 out of 20), and HEED™ (8 out of 20).
The software that received the highest scores (Green Building Studio™, IES<VE>™,
and Ecotect™) had a wider range of building performance simulations. Some of the
58
outputs included in all of the top three software that set them apart from the others
included tools for lifecycle cost and assessment, LEED integration, and wind energy
potential. The results of the available outputs evaluation are shown in Figure 4-5.
Figure 4-5. Available Outputs
4.1.5 Cumulative Score
The cumulative scores used in the BEM tool evaluation were calculated by first
converting the criteria scores into percentages. The final score (Σc ) was the sum of
these percentages with each criterion receiving the same weight. The final score was
calculated by equation [4-1]:
Σc = c1+c2+c3+c4 .[4-1]
where: c1 = User friendliness; c1 = x/8 c2 = Interoperability; c2 = x/5, c3 = Available
inputs; c3 = x/25 c4 = Available outputs; c4 = x/15, x = number of subcriteria supported
by BEM tool for the respective criterion
59
Results of the overall scores are illustrated in Figure 4-6. IES<VE>™ received the
highest score in the evaluation (3.38 out of 4). Ecotect™ received the second highest
score (3.14 / 4), and Green Building Studio™ received the third highest (3.06 / 4). These
three BEM tools were selected for use in the case study.
Figure 4-6. Overall scores of the BEM tool initial evaluation
Figure 4-7 depicts the overall versatility of the BEM tools in terms of available
inputs and available outputs. BEM tools that had high scores in available inputs (12.5 -
25) and available outputs (10 - 20) fell in quadrant B. BEM tools with higher scores in
available outputs (10 – 20) and lower scores in available inputs (0 – 12.5) fell in quadrant
A; tools with lower scores in available outputs (0 – 10) and higher scores in available
inputs (12.5 – 25) fell in quadrant C; and tools that had low scores in both available
inputs (0 – 12.5) and available outputs (0 – 10) fell in quadrant D.
BEM tools that were in quadrant A (higher scores in available outputs and lower
scores in available inputs) included Energy10™, Tas™, Hevacomp™, and Visual
DOE4.0™. BEM tools that were in quadrant B (higher scores for both available inputs
and available outputs) were Green Building Studio™, eQuest™, Ecotect™, and
60
IES<VE>™. EnergyPlus™ and EcoDesigner™ fell in quadrant C, which is characterized
by limited outputs with a wider range of inputs; and HEED™ and DesignBuilder™ fell in
quadrant D (low scores in both available inputs and outputs).
Figure 4-7. The scores for available inputs and available outputs of the BEM tools
4.2 Case Study
The top three BEM software tools (Ecotect, Green Building Studio, and IES<VE>)
were used in the case study. Simulations of each building were performed by each BEM
tool and assessed energy usage, daylighting, and natural ventilation. Overall, the
simulations showed that the LEED certified building (Rinker Hall) would perform better
than the non-LEED certified building (Gerson Hall) in regards to annual energy usage
(by both overall energy use and EUI) and daylighting performance for the selected
61
rooms. Simulation results showed that Gerson Hall performed better than Rinker Hall in
regard to natural ventilation potential.
4.2.1 Energy Usage
Regarding energy usage, Rinker Hall, the LEED-certified building, performed better
than Gerson Hall in both total annual energy usage and in energy use intensity (EUI).
This was true in all three BEM programs (Figure 4-8). Ecotect™ simulations showed that
Rinker Hall would consume less energy than Gerson Hall (56% difference between
EUIs). Green Building Studio™ calculations also showed that Rinker Hall would
consume less energy than Gerson Hall (20% difference between EUIs). Similarly, IES
<VE>™ simulations estimated that Rinker Hall would consume less energy than Gerson
Hall (36% difference between EUIs).
Figure 4-8. Energy use intensity (EUI) comparison by building and by BEM tool. Dotted
line denotes the CBECS national median EUI for educational building types (104 kBtu/SF)
As of 2003, the CBECS national median energy use intensity for Education
(College/University) building types is estimated to be 104 kBtu/SF. This serves as a
62
baseline value to compare the energy simulations against. The lower the EUI, the more
energy efficient the building is. In all three BEM tools, Rinker Hall was simulated to
perform better than the national average. Ecotect™ simulations estimated an EUI of 45
kBtu/sf; Green Building Studio™ simulated an EUI of 58 kBtu/sf; and IES<VE>™
simulated an EUI of 61 kBtu/sf. When compared against the CBECS national average,
simulations of Gerson Hall had mixed results. Green Building Studio™ estimated that it
would perform better with an EUI of 73 kBtu/sf; the Ecotect™ simulation estimated that it
would perform very close to the national average with 103.18 kBtu/sf; and the
IES<VE>™ simulation showed that Gerson Hall would exceed the national mean with an
EUI of 126 kBtu/sf. For all three BEM software, the energy use breakdowns for the two
buildings showed that the greatest amount of energy was used for space cooling (Figure
4-9).
Figure 4-9. Energy use breakdown for two buildings used in case study using three
BEM tools.
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Ecotect™ simulations broke down energy use into two categories: space heating
and space cooling. For both Rinker Hall and Gerson Hall, a larger proportion of energy
was used for space cooling than for space heating. Green Building Studio™ broke down
energy use based on percentage of energy used for space heating, heat rejection, fans,
pumps & auxiliary, space cooling, exterior loads, miscellaneous equipment and lights.
Again, the largest proportion of energy was used for space cooling for both Rinker Hall
and Gerson Hall. Energy use breakdowns obtained by IES<VE>™ simulations were
broken down into the categories of space heating, fans, pumps & auxiliary, space
cooling, miscellaneous equipment, and lighting. Results for Rinker Hall and Gerson Hall
showed again showed that the largest proportion of energy was used of space cooling.
4.2.2 Daylighting Performance
The daylighting performance of each building could be compared within each
program, but results could not be compared between the three BEM programs due to the
fact that daylight factor was not calculated in a consistent manner. Only Ecotect™ and
IES <VE>™ allow the user to specify the placement of sensor points at which the
daylight level is measured. None of the three tools allow the user to specify the date and
time at which the daylight factor is calculated.
The rooms in Rinker Hall had higher daylight factors than their counterparts in
Gerson Hall, but with some exceptions (Table 4-1). Within each BEM tool, Rinker Hall’s
conference room, classroom, and graduate student office suite performed better than
those in Gerson Hall. The faculty office had mixed results. Ecotect™ and Green Building
Studio™ predicted higher daylight factors for the office in Gerson Hall, while IES <VE>™
estimated the faculty office in Rinker Hall to perform better. Overall Rinker Hall appeared
to have better daylighting performance than Gerson Hall based on the rooms simulated
64
in the study. This may be attributed to Rinker Hall having a higher window to wall ratio
(see Table 3-1).
Table 4-1. Comparison of daylight factors for the selected rooms. Room Function
Building
Ecotect
Green Building Studio
IES<VE>
Conference
Rinker Hall Gerson
11.48% 6.30% 13.70%
Room Hall 3.37% 0.70% 4.80% Rinker Hall 2.74% 0.30% 6.40% Gerson Faculty Office Hall 3.22% 1.00% 5.00% Rinker Hall 3.98% 0.80% 3.80% Gerson Classroom Hall 3.00% 0.20% 1.10% Rinker Hall 3.89% 0.90% 2.60% Gerson Graduate studio Hall 1.79% 0.50% 3.10%
Highlighted values are greater than the minimum required daylight factor (2%) for adequate daylighting. 4.2.3 Natural Ventilation
Each of the three BEM software tools assessed natural ventilation in different ways
(Table 4-2). Green Building Studio™ provided outputs related to the amount of energy
that could be saved through the use of natural ventilation. Natural ventilation potential in
Ecotect™ was obtained by running two simulations – one with operable windows
activated (allowing for natural ventilation at optimal times of the year) and one without
operable windows activated. IES<VE>™ simulated natural ventilation in terms of
average airflow (CFM) per square foot.
Green Building Studio™ simulations showed that Gerson Hall (potential annual
energy savings of 57,883 kWh) could possibly save more energy (44% difference)
through natural ventilation than Rinker Hall (potential annual energy savings of 32,254
kWh). Potential energy savings from natural ventilation were calculated in Ecotect™ by
subtracting the overall energy use of the models with natural ventilation activated from
65
energy use values of the benchmark models. Ecotect™ simulations also showed that
Gerson Hall (potential savings of 142,043 kWh) could possibly save more energy (35%
difference) than Rinker Hall (potential savings of 92,516 kWh). IES<VE>™ was able to
assess natural ventilation by providing average annual infiltration rates (cfm) for each
zone. Gerson Hall had an average natural ventilation rate of 0.033 CFM per square foot
averaged over the entire inhabitable building floor area compared to Rinker’s average
natural ventilation rate of 0.022 CFM per square foot. Thus Gerson Hall seemed to
provide a 33% higher ventilaton rate than Rinker Hall.
Table 4-2. Natural Ventilation Simulation Results for three BEM tools. Potential energy savings from natural ventilation (kWh)
Rinker Hall 92,516 Ecotect Gerson Hall 142,043
Potential energy savings from natural ventilation (kWh) Rinker Hall 32,254 Green Building
Studio Gerson Hall 57,883 Average CFM per square foot from natural ventilation
Rinker Hall 0.022 IES<VE> Gerson Hall 0.033
The probable reason why Gerson Hall outperformed Rinker Hall based on natural
ventilation results obtained by each of the three BEM tools, is Gerson Hall’s orientation
towards prevailing winds. Whereas Rinker Hall is oriented longitudinally north to south,
Gerson Hall is oriented east to west (Figure 4-10).
Prevailing winds in the summer months for these building locations come from the
south-southwest. By exposing a larger surface area of the building to the prevailing
winds (by orienting itself east to west), Gerson Hall has more interior rooms exposed to
prevailing wind-assisted natural ventilation for times of year when natural ventilation is
beneficial to reducing the cooling load.
66
Figure 4-10. Diagram of building orientations relative to summertime prevailing winds.
4.3 Re-Evaluation of Building Energy Modeling Tools Used in the Case Study
An updated scoring system was used to re-evaluate BEM tools used in the case
study (Figure 4-11). The scoring system was based on the one used in the initial
evaluation. Adjustments were made based on information gathered during the case
study. The four criteria used in the re-evaluation were user friendliness, interoperability,
versatility, and speed. Versatility encompasses the range of both available inputs and
available outputs (which were individual criteria in the initial evaluation). The criterion of
speed was added to the re-evaluation. This criterion refers to calculation speed, that is,
the amount of time that each BEM tool took to complete each of the three simulations.
The comprehensive score for each BEM tool was calculated as the sum of the
individual criterion scores. Each criterion was scored as the fraction of subcriteria
supported by the BEM tool over the total number of subcriteria. For the criterion of
interoperability, nine sub-criteria were included in the checklist. Thus, the criterion score
was calculated as x/9, where x = number of subcriteria supported by the BEM tool.
Similarly for user-friendliness, which held 11 sub-criteria, the criterion score was
determined as x/11. The criterion score for versatility was calculated as x/47 (for 47
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subcriteria), and the criterion score for speed was calculated as x/6 for (six subcriteria).
The highest possible score for each criteria was 1.00, and the highest possible
comprehensive score was 4.00. The cumulative scores used in the BEM tool re-
evaluation were calculated by first converting the criteria scores into percentages. The
final score (Σc ) was the sum of these percentages with each criterion receiving the
same weight. The final score was calculated by equation [4-2]:
Σc = c1+c2+c3+c4 [4-2]
where: c1 = Interoperability; c1 = x/9, c2 = User Friendliness; c2 = x/11 c3 = Versatility; c3 = x/47, c4 = Speed; c4 = x/6, x = number of subcriteria supported by BEM tool for the respective criterion
Figure 4-11. Re-evaluation scoring system with criteria and subcriteria
68
Based on the un-weighted results from the re-evaluation, IES<VE>™ appeared to
be the strongest of the three BEM tools used in the case study. This was largely due to
IES <VE>™ receiving high marks in user-friendliness (score 0.73 out of 1.00) and
versatility (0.91 out 1.00). Figure 4-12 illustrates the un-weighted comprehensive scores
obtained by the re-evaluation. As mentioned, IES<VE>™ appeared to be the strongest
with cumulative score of 2.75 out of 4 possible points. Green Building Studio™ had the
second highest score of 2.41 out 4; and Ecotect™ had the lowest score of the three with
a total of 2.14 out of 4.
Figure 4-12. Re-evaluation un-weighted cumulative scores
A matrix was developed applying various weights to the criteria based on the user’s
order of importance. The criterion first in importance was multiplied by a factor of four,
second by a factor of three, third by a factor of two, and fourth by a factor of one. This
matrix yielded 24 possible combinations (Table 4-3).
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Among the 24 possible weightings, IES <VE>™ achieved the highest score of 21.
Based on the research findings Green Building Studio™ is recommended when speed
is the highest priority for the user, and interoperability the second highest; and
when the order of importance is speed, user-friendliness, interoperability, and versatility.
The study recommends IES <VE>™ for any other combination of the criteria.
Table 4-3. Re-evaluation matrix with various weightings Order of Importance Software
selection 1 2 3 4 Weight 1
Interoperability User-friendliness
Versatility Speed IES<VE>
Weight 2
Interoperability User-friendliness
Speed Versatility IES<VE>
Weight 3
Interoperability Versatility User-friendliness
Speed IES<VE>
Weight 4
Interoperability Versatility Speed User-friendliness
IES<VE>
Weight 5
Interoperability Speed Versatility User-friendliness
IES<VE>
Weight 6
Interoperability Speed User-friendliness
Versatility IES<VE>
Weight 7
User-friendliness
Interoperability Versatility Speed IES<VE>
Weight 8
User-friendliness
Interoperability Speed Versatility IES<VE>
Weight 9
User-friendliness
Versatility Interoperability Speed IES<VE>
Weight 10
User-friendliness
Versatility Speed Interoperability IES<VE>
Weight 11
User-friendliness
Speed Interoperability Versatility IES<VE>
Weight 12
User-friendliness
Speed Versatility Interoperability IES<VE>
Weight 13
Versatility Interoperability User-friendliness
Speed IES<VE>
Weight 14
Versatility Interoperability Speed User-friendliness
IES<VE>
Weight 15
Versatility User-friendliness
Interoperability Speed IES<VE>
Weight 16
Versatility User-friendliness
Speed Interoperability IES<VE>
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Table 4-3. Continued Order of Importance Software
selection 1 2 3 4 Weight 17
Versatility Speed Interoperability User-friendliness
IES<VE>
Weight 18
Versatility Speed User-friendliness
Interoperability IES<VE>
Weight 19
Speed Interoperability User-friendliness
Versatility GBS
Weight 20
Speed Interoperability Versatility User-friendliness
GBS
Weight 21
Speed User-friendliness
Interoperability Versatility GBS
Weight 22
Speed User-friendliness
Versatility Interoperability IES<VE>
Weight 23
Speed Versatility Interoperability User-friendliness
IES<VE>
Weight 24
Speed Versatility User-friendliness
Interoperability IES<VE>
Various weightings were based on multipliers applied to the order of importance for each criterion. The first most important criterion score was multiplied by a factor of four, the second most important multiplied by a factor of three, third most important multiplied by a factor of two, and fourth most important multiplied by a factor of one.
A detailed set of results from the re-evaluation is shown in Tables 4-4 through 4-7.
This provides potential BEM users with a breakdown of the re-evaluation in terms of
availability of each subcriteria used in the scoring system. Users may refer to this table
to ensure that certain desired functions are included in the BEM tool they select. This
table served as a checklist during the re-evaluation. For each subcriteria, the BEM tool
was scored with a 1 if the capability is included in the software, a 0 if it was not, and 0.5
if the capability was included but with limitations.
Ecotect™ demonstrated the highest degree of interoperability (Table 4-4) and
obtained a score of 6.5 out of 9 possible points in the interoperability evaluation criterion.
IES<VE>™ had the second highest score (5.5 out of 9) and Green Building Studio™
demonstrated the lowest degree of interoperability (score 4 out of 9). Table 6 provides
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the checklist and scores for each of the three BEM tools for the criterion of
interoperability.
Table 4-4. Re-evaluation of three BEM tools for interoperability Subcriteria Ecotect Green Building Studio IES<VE> Geometry translation (from Revit Architecture as gbXML file)
0.5 0.5 0.5
Material translation (from Revit Architecture as gbXML file)
0.5 0 0.5
Openings (doors and windows) translation (from Revit Architecture as gbXML file)
0.5 0.5 0.5
Google SketchUp plugin 0 0 1 Import DXF 1 0 1 Import IFC 1 0 0 Import gbXML 1 1 1 Export gbXML 1 1 0 Export analysis data to Microsoft Excel
1 1 1
Total Points (out of 9)
6.5
4
5.5
Percentage score 0.72 0.44 0.61 For each subcriteria the BEM tool received 1 point if the capability was included, 0 points if not included, and 0.5 if the capability was included but with errors or limitations.
The only program that Ecotect™ did not interoperate with was SketchUp™. A
potential strength of Ecotect™ was the ability to import IFC files. None of the other BEM
tools had this capability. All three BEM tools allowed for gbXML files to be imported.
However, the export of the BIM models from Revit™ as gbXML files to each of the three
BEM tools showed errors in certain inputs. In all three software, errors were found in the
geometry translation, material translation, and openings translation from the Revit
models. When these inputs were exported from Revit™ with errors, the BEM tool
received a score of 0.5 on the checklist. Green Building Studio™ did not receive material
data from the gbXML file (and thus received a 0 in this subcriteria) and these inputs had
to be re-entered. IES<VE>™, which received the second highest score for
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interoperability provides a SketchUp™ plugin, but does not have the capability to export
gbXML files. All three BEM tools were able to export analysis data to Microsoft
Excel™.IES<VE>™ received the highest score out of the three BEM tools for user
friendliness supporting eight out of the 11 subcriteria (Table 4-5). Green Building
Studio™ had the second highest score (6.5 out 11) and Ecotect™ had the lowest score
of the three (6 out of 11). IES<VE>™ benefitted from the inclusion of a gbXML model
error check, a secondary model error check that is run automatically before initializing
simulations, and an automatic report generator.
Table 4-5. Re-evaluation of three BEM tools for user friendliness Subcriteria Ecotect Green Building Studio IES<VE> Help file 1 1 1 User support forum 1 1 1 Simple user interface 0 1 0 Default libraries / templates 1 1 1 gbXML import model error check 0 0 1 Model error check during simulation 1 0 1 Automatic report generator 0 1 1 3-D model GUI (graphical user interface)
1 0 1
Requires minimal expertise 0 0.5 0 Design alternatives assistance 0 1 0 Ability to edit building geometry in program
1 0 1
Total Points (out of 11)
6
6.5
8
Percentage score 0.55 0.59 0.73 For each subcriteria the BEM tool received 1 point if the feature is included, 0 points if not included, and 0.5 if the feature was included but with limitations.
In the re-evaluation, the versatility evaluation criterion was comprised of subcriteria
in the categories of available inputs, versatility of inputs, available outputs, and versatility
of outputs. Availability of inputs and outputs refers to the range of inputs and outputs
provided by the BEM software. Versatility of inputs and outputs refers to the ability of
users to define and customize the inputs and outputs. Overall, IES<VE>™ had the
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highest score (43 out of 47 possible points) and appeared to be the most versatile of the
three BEM tools assessed in the re-evaluation (Table 4-6).
Ecotect™ was the second most versatile with a score of 41 out of 47, and Green
Building Studio™ appeared to be the least versatile with a score of 23 out of 47. The
scoring for each subcriterion was as follows: 1 if the input/output is included, 0.5 if the
input/output included but with limited options, and 0 if the input/output is not included.
Table 4-6. Re-evaluation of three BEM tools for versatility. Subcriteria Ecotect Green Building Studio IES<VE> Versatility of inputs User-defined constructions 1 0.5 1 User-defined occupancy schedule 1 0 1 User-defined equipment/lighting schedule
1 0 1
User-defined systems (HVAC) 1 0 1 User-defined time step for calculations
0.5 0.5 0.5
Zone-by-zone inputs 1 0 1 Model builder 1 0 1 Versatility of outputs
User-defined time step 0.5 0 0.5 User-defined reports/graphical outputs
1 0 1
Graphical analysis over model 1 0 1 Animations 0 0 1 Room/zone level analysis 1 0 1 Graphical comparisons between design iterations
0 1 1
Available Inputs
HVAC type 1 1 1 Heat recovery system 1 0 0 Glazing specifications (low-e, tint, U value, visible transmittance
1 1 1
Automated lighting controls 1 1 1 Constructions (walls, roof, floor) 1 1 1 Albedo 1 1 1 Shade walls / louvers 1 0 1 Lighting power density 1 1 1 HVAC design flow 1 0 1
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Table 4-6. Continued Subcriteria Ecotect Green Building Studio IES<VE> Local terrain 1 1 1 Geographic location / climate 1 1 1 Occupancy schedule 1 0 1 Equipment / lighting schedule 1 0 1 HVAC schedule 1 0 1 Required interior design temperature (heating / cooling setpoint)
1 1 1
Equipment power density 1 1 1 Fuel type 1 1 1 System energy efficiency 1 0 1 User-defined fan power 1 0 1 Operable window (openings to allow for natural ventilation)
0 1 1
Operable windows schedule 0 0 1 Available Outputs
Energy usage 1 1 1 Carbon emissions 1 1 1 Resource management 1 1 1 Thermal analysis 1 0 1 Heating / cooling load breakdown 1 1 1 Solar analysis 1 0 1 Daylighting assessment 1 1 1 Lighting design 1 0 1 Lifecycle cost analysis 1 1 1 Ventilation and airflow analysis 1 1 1 Water usage 1 1 0 Design alternative comparisons 0 1 0 Total Points (out of 47)
41
23
43
Percentage score 0.87 0.49 0.91 For each subcriteria the BEM tool received 1 point if the feature is included, 0 points if not included, and 0.5 if the feature was included but with limitations.
The criterion of speed was evaluated for the three BEM tools used in the case
study by recording the amount of time each BEM tool took to perform each simulation
(energy, daylighting, and natural ventilation). Results are shown in Figure 4-7. Green
Building Studio™ received the highest score for speed with 6 out of 6 possible points.
IES<VE>™ received the second highest score (3 out of 6) and Ecotect™ received the
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lowest score (0 out of 6). The major advantage of Green Building Studio™ in regard to
this criterion had to do with its calculation engine being server based. Calculations were
performed online which decreased calculation times in all three analyses types.
IES<VE>™, which had the second highest score was able to perform the three
simulation types in under 1 hour. Ecotect™, which had the lowest score for speed, had
simulation times that lasted several hours.
Table 4-7. Re-evaluation of three BEM tools for speed Subcriteria Ecotect Green Building Studio IES<VE> Energy simulation time under 1 hour 0 1 1 Energy simulation time under 10 minutes
0 1 0
Daylighting simulation time under 1 hour
0 1 1
Daylighting simulation time under 10 minutes
0 1 0
Ventilation simulation time under 1 hour
0 1 1
Ventilation simulation time under 10 minutes
0 1 0
Total Points (out of 6)
0
6
3
Percentage score 0 1.0 0.5
4.4 Guidelines for using Ecotect™, Green Building Studio™ and IES<VE>™
During the case study, a log was maintained noting problems and observations for
each of the three BEM tools used in the case study. The steps in the energy modeling
process that were analyzed were model preparation in Revit™, model preparation in
BEM tool, weather data, energy analysis, daylighting analysis, ventilation analysis, and
schedule implementation. The following section summarizes these observations (which
are provided in full detail in the Appendix C) for each BEM tool.
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4.4.1 Model Preparation in Revit
During this step of the energy modeling process, it was important to check that all
rooms were modeled correctly and bounded by the correct elements in plan and section.
If there were errors in how the rooms were modeled, Revit™ did not allow the BIM
model to be exported as a gbXML. Problems encountered during this stage of the
energy modeling process included the following:
• Inconsistent phase assignments between room elements and other building elements
• Overlapping rooms
• Overlapping room-bounding objects
• Missing objects (e.g. shade walls) in the gbXML model
Special attention should be given to rooms and room-bounding objects. It is important to
ensure that all interior spaces are modeled as rooms; otherwise gbXML will recognize
these as exterior spaces.
4.4.2 Model Preparation in Building Energy Modeling Software
The model preparation portion of the energy modeling process refers to the work
that was done on the model between importing gbXML files to the BEM, and initializing
the simulation in BEM. The amount of inputs needed in model preparation for each BEM
software varied. Green Building Studio™, which did not have model-building functions,
required minimal inputs to run a base simulation. Model preparation in Ecotect™ and
IES <VE>™ required users to run error checks before simulations could start. Both BEM
tools have model-building functions that allow users to fix model errors. Automatic error
reports were generated by both BEM tools and allow users to locate errors in the model
with relative ease.
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Green Building Studio™ required the least amount of model preparation before
running the initial simulation. The gbXML models of Rinker Hall and Gerson Hall
exported from Revit™ were loaded directly into the Green Building Studio™ web-based
analysis engine. In the online interface, users fill out a questionnaire about building type
and location before the base simulation can run. After running a base simulation,
iterations of the building model can be run by adjusting the building specifications in the
“project defaults” tab. In this window, building specifications related to system types,
constructions, and glazing are input. While this allows for simulations to run quickly and
require minimal inputs, it limits the amount of editing a user may perform on the building
model in Green Building Studio™. Any changes to the building geometry and the interior
organization of zones must be performed in Revit™ (or other gbXML-enabled BIM or 3D
modeling platform). While gbXML models may be inspected using a third- party 3D
model viewer, Green Building Studio™ is unable to edit potential building geometry
errors that occur during the translation of the BIM model to gbXML file.
Model editing proved to be useful in Ecotect™ and IES<VE>™ as many errors
were found in the gbXML files. This capability is enhanced in both tools by including
error detections. Ecotect™’s error detection occurs when the first simulation is initialized
and provides a list of errors detected and corresponding location in the model (e.g.
zone28, surface2093). IES<VE>™ performs its error detection when the gbXML file is
imported. IES<VE>™ error reports also include corresponding locations in the model to
the errors found. The most common error in both programs during the case study was
errant holes in surfaces. Other major errors encountered in the gbXML files imported
into the BEM tools were missing components, such as shade walls. Such components
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were rebuilt in Ecotect™ and IES<VE>™. Due to limitations of the software, these
components were omitted from the Green Building Studio™ energy models. Like Green
Building Studio™, Ecotect™ and IES<VE>™ also may require to re-input envelope
constructions. Both Ecotect™ and IES<VE>™ support a greater degree of versatility in
specifying envelope constructions by allowing users to specify construction layers and
layer properties. This is in contrast to Green Building Studio™, which only allows users
to specify construction types included in a drop down menu.
4.4.3 Weather Data Acquisition
The proximity of weather data sources to actual building locations for the three
BEM tools ranged from 4.0 miles to 0.8 miles (Figure 4-13). To obtain Gainesville
weather data, the weather file for Ecotect™ had to be downloaded from the DOE
EnergyPlus website. By comparison, Green Building Studio™ and IES<VE>™ had
weather data libraries with Gainesville weather data already built into the software.
Figure 4-13. Location of weather data for three BEM tools in proximity to case study
buildings
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In Ecotect™ weather data for Gainesville was loaded from the DOE Energy Plus
website. The Gainesville weather data file came from information gathered at the
Gainesville Regional Airport (located roughly 4 miles from the University of Florida
campus). This was also the location of the weather data file for IES<VE>™. By
comparison, the weather data file acquired for Green Building Studio™ came from a
weather database located on the University of Florida campus and much closer to the
actual building locations.
4.4.4 Schedule Implementation
The three BEM tools allow users to implement schedules with varying degrees of
customization. In particular the research sought to implement schedules for occupancy,
equipment usage, electrical lighting usage, and natural ventilation. While Green Building
Studio™ was only able to implement an occupancy schedule, Ecotect™ and IES<VE>™
were able to implement all four with varying degrees of customization. Both Ecotect™’s
and IES<VE>™’s schedule editors allow the user to create profiles on the daily, weekly,
and annual basis. Both BEM tools provide default schedule that may be used as a
template and tailored to more specific conditions and schedules on the project.
Ecotect™: Ecotect™ allows users to implement all four of the schedules (also
called “operational profiles” in Ecotect™). The schedule library provides several typical
operational profiles that may be adjusted in the schedule editor (Figure 4-14). Using the
schedule editor, hourly operational profiles may be created for occupancy, equipment
usage, electrical lighting, and natural ventilation. Users can click and drag points on the
hourly operational profile to adjust and create new schedules, or input the values into the
table.
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Figure 4-14. Ecotect™ Schedule Editor
Each of these schedules was implemented for all zones using adjusting the zone
properties. Number of occupants and occupancy schedules were input under the tab
“occupancy.” A generalized schedule assuming electrical lighting and room equipment
run at the same time was input under the tab “internal gains.” The ventilation schedule
was developed using the guidelines set forth by ASHRAE Standard 55.2004 (Figure 4-
15). Under the tab “infiltration rate,” the study referred to the weather file to develop a
natural ventilation schedule that was active for outdoor temperatures that fall within the
ASHRAE Standard 55.2004 thermal comfort range. As per Standard 55.2004, a wider
comfort range is allowed when relying on natural ventilation. This meant that the
ventilation schedule was developed so as to trigger the operable windows to be 100%
open during the times of year when the outdoor temperature was within the acceptable
comfort range for natural ventilation. Using the weather data provided by Ecotect, the
schedule was developed by identifying those times of year and manually inputting them
into the operable window schedule.
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Figure 4-15. Mean monthly average temperatures and corresponding comfort ranges.
The shaded area refers to acceptable air-conditioned thermal comfort ranges, and the black lines refer to acceptable thermal range for natural ventilation. Dotted lines denote the acceptable thermal comfort range for given mean monthly outdoor temperatures (ASHRAE 2004).
Green Building Studio™: The only schedule that could be implemented into the
energy models in Green Building Studio™ was the occupancy schedule. The option
“School, year-round” was selected from a drop down menu during the initial
questionnaire when the gbXML file was initially imported into Green Building Studio™.
Users are unable to create their own schedules, or adjust occupancy schedules in the
drop down menu. For this reason, Green Building Studio™ is not recommended for
calibration purposes.
IES<VE>™: Schedule is handled in the Apache module with the icon for Apache
profile database manager. Each room has been assigned a profile from a drop down
menu in the ModelIT module. These can then be customized by editing the profiles in
Apache. Users may create their own schedules here as well allowing for a degree of
customization (Figure 4-16). This allows users to input values in the schedule either
graphically or numerically.
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Figure 4-16. IES<VE>™ schedule editor interface
This was especially useful when developing operational profiles for operable
windows. Unlike Ecotect™, which required users to develop daily schedules based on
climate data, IES<VE>™’s schedule (profile) editor allows users to devise schedule
based on formulas as well using the “modulating formula profile creation” tool (Figure 4-
17). In this way, the operable window schedule was input by triggering operable
windows to open based on thermal parameters. These were input as temperature
ranges derived from ASHRAE Standard 55.1. Operable windows were open 100% when
the outdoor temperature was less than 78˚ F and greater than 70˚ F.
4.4.5 Energy Analysis
Each BEM tool reported energy usage in different ways and had varying ranges of
capabilities. The extent to which users are able to customize reports and energy
analyses varied as well. Green Building Studio™, which was the quickest to generate
energy reports, was limited in output options. Ecotect™ and IES<VE>™ provided more
versatility in outputs, but had longer calculation times (under one hour calculation times
for IES<VE>™ while Ecotect™ calculations could take several hours). In particular,
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IES<VE>™ and Ecotect™ differed from Green Building Studio™ by allowing users to
specify thermal zones within the model and simulation time steps for energy analysis.
Figure 4-17. IES<VE>™ Modulating formula profile creation interface allows schedules
to be derived from thermal parameters.
Ecotect™: Ecotect™ runs energy analyses through the drop down menu
“Calculate >> Thermal Analysis,” and results are viewed in the Analysis module under
the tab “Resource Consumption.” Simulations and reports may be broken down into
daily time steps and on a zone-by-zone basis (depending on which zones are selected
for the simulation run). Various outputs may be selected, displayed and compared within
the analysis tab. These outputs included:
• Hourly temperature profile • Hourly heat gains/losses • Heating/cooling loads • Daily to annual energy use • Daily load matching • Hourly solar collection • Hourly to annual electric use • Hourly to annual natural gas use • Hourly to annual coal use
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• Hourly to annual fuel oil use • Hourly to annual kerosene use
Green Building Studio™: Energy analyses in Green Building Studio™ may be
viewed in either the overall report for each simulation run, or in the data run charts which
compare the energy performance for different runs and projects (Figure 4-18). The run
charts break down the energy usage into nine categories:
• Area lights • Exterior usage • Miscellaneous equipment • Space cooling • Heat rejection • Vent fans • Pumps auxiliary • Space heat • Hot water
Figure 4-18. Green Building Studio™ run chart comparing buildings used in case study
IES<VE>™: Thermal analysis was conducted using the Apache module for
calculations, and the Vista module for results analysis. Users should make sure to run
an update of the SunCast calculations before performing energy analyses in Apache.
The Apache Module provides the interface to specify constructions, systems, and
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schedules. The Apache Dynamic Simulation was selected and was run from Jan 1 to
December 31 with a 15-minute time step (by default). This specifies that the simulation
will calculate values for the entire year, with a resolution based on 15 minute intervals.
Similarly with the Analysis tool from Ecotect™, the Vista Module for IES<VE>™ provides
users with the ability to customize reports and the presentation of data. The “project
summary” base report generated by IES<VE>™ for energy analysis breaks down the
annual energy usage into the following categories:
• Heating • Cooling • Fans / pumps • Lights • Equipment 4.4.6 Daylighting Analysis
Each BEM tool uses a different methodology for assessing daylighting
performance. The range of outputs differed as well. Green Building Studio™ provided
outputs in the units of glazing factor, while Ecotect™ and IES<VE>™ provided outputs in
daylight factor (the inverse of glazing factor). Ecotect™ and IES<VE>™ were also able
to provide graphical outputs with daylight factor analysis grids displayed over the floor
plan. None of the software allow the user to specify the date and time at which the
daylight simulation is performed, and only Ecotect™ and IES<VE>™ allow the user to
specify the placement of sensor points. All three BEM tools were set to CIE uniform sky
conditions for all simulations.
Ecotect™: While the versatility of Ecotect™’s daylight simulation inputs were
limited (because Ecotect™ was unable to specify the date and time of the simulation),
users are able to customize both the analysis grid and presentation of daylight factor
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data in analysis graphs and reports. The analysis grid is helpful for users to locate areas
in zones that do not have adequate daylighting (2% daylight factor by LEED standards),
and provides graphical cues as to where to place electrical lighting efficiently, and
alternative glazing strategies to improve daylighting performance. One disadvantage of
Ecotect™’s daylighting simulation engine is that it was not uncommon for simulation
runs to take several hours. Using simplified models can reduce the calculation time.
However, it was noted that reducing the complexity of the gbXML file export from Revit™
led to more errors in the model upon importing it into Ecotect™.
Green Building Studio™: Glazing Factor (inverse of daylight factor) is the
parameter that Green Building Studio™ uses to assess daylighting performance.
Results are broken down on a zonal basis. Green Building Studio™ does not allow the
user to specify sensor positions in the model. This is a major disadvantage for users
simulating daylighting performance for specific areas within zones (e.g. the location of a
desk). Users are also unable to specify the date and time of the simulation run. Without
any of this information, it is difficult to utilize a single simulation run’s daylighting data.
These reports are useful to compare design alternatives. While daylighting analysis in
Green Building Studio™ is not very versatile, simulation runs are much quicker, only
taking a matter of seconds (dependent on user bandwidth). The daylighting results are
also tailored to show effectiveness of the building’s daylight performance compared to
the requirements for LEED credits. These credits are awarded if the building is able to
provide a glazing factor of 0.02 for at least 75% of the regularly occupied floor area.
IES<VE>™: Daylighting was performed using the FlucsDL module in IES<VE>™.
Prior to running the FlucsDL simulation, the SunCast module was used to update the
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shading calculations. Graphical outputs called “daylight gradients” over the floor plans
were very helpful to locate errors in the model. The daylight gradients are similar to
Ecotect™’s analysis grid displaying color gradients to daylight factor values gridded over
the floor plan. The height of the grid can also be specified and by default is set at the
height of a typical working plane. As with the energy models in Ecotect™ and Green
Building Studio™, shading devices for Rinker Hall were lost in the gbXML file import and
had to be modeled again in IES<VE>™. Daylighting simulations can be run for any hour
of any day throughout the year. This allows for daylight autonomy to be calculated.
4.4.7 Natural Ventilation Analysis
There is a wide range of capabilities for BEM tools in the category of natural
ventilation. Potential energy savings from natural ventilation could be calculated using all
three BEM tools used in the case study. However, since the use of natural ventilation
and its resultant energy savings are dependent on the unpredictable variables of
weather and occupancy behavior (i.e. opening operable windows), natural ventilation
simulations must make broad generalizations and assumptions. All three software rely
on the Sherman-Grimsrud ventilation method to calculate natural ventilation potential.
This calculation is based on hourly wind speed and indoor versus outdoor temperatures
to model air change. ASHRAE Standard 55 was used to determine adequate monthly
comfort ranges. Users should note that this standard affords a wider range of thermal
comfort when relying on natural ventilation for cooling based on changes in occupants’
“thermal sensation” or “adaptive thermal comfort.” A study conducted by ASHRAE
revealed that occupants, due to psychological factors, have a wider thermal comfort
range when relying on natural ventilation.
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Green Building Studio™: By default, natural ventilation simulations in Green
Building Studio™ were set to the following conditions, which could not be changed by
the user:
• Building and openings are designed to allow for the stack effect and/or cross ventilation
• Natural ventilation is used during thermal comfort zone periods (GBS does not specify what the thermal comfort zone is).
• Air changes per hour is less than 20 ACH • Entire window area is operable
Based on these assumptions and local climatic conditions, Green Building Studio™
provides a concise report on natural ventilation potential. This report includes the
following outputs:
• Total hours mechanical cooling required • Possible natural ventilation hours • Possible annual electric energy savings • Possible annual electric cost savings • Net hours mechanical cooling required
These values are averaged over the entire building and cannot be broken down on
a zone-by-zone basis. Green Building Studio™ also does not provide a platform to
conduct microclimate analysis within zones using computational fluid dynamics (CFD) to
simulate airflow through spaces.
Ecotect™: Users are able to estimate potential energy savings from natural
ventilation by comparing two energy simulation runs: one without operable windows
activated and one with operable windows activated by assigning an operational profile
(schedule) to operable windows. The development of the operable window schedule can
be informed by climate data reports that indicate days throughout the year when the
climate is within the comfort range. This can was done by selecting “temperature” from
the thermal analysis tool. This created a graph that displayed indoor and outdoor
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temperatures. When the outdoor temperature is within the ASHRAE 55-defined comfort
range, operable windows may be open to reduce the cooling demand. During these
periods in the operational profile, the user may specify percentage of the window area
that should be open. As a standalone software, Ecotect™ does not provide users with a
platform to conduct microclimate CFD analysis, although it is possible to use a third
party software such as WinAir to conduct CFD simulations. This file may be brought
back into Ecotect™ and used in the analysis grid to provide users with a visualization of
air flow through zones.
IES<VE>™: Using a methodology similar to the one described in the previous
section on natural ventilation in Ecotect™, users may also estimate potential energy
savings from natural ventilation in IES<VE>™. Two simulation runs are needed, one
without operable windows, and one with operable windows activated. The difference
between the two is the potential energy savings from natural ventilation. A major
advantage to IES<VE>™ is that the operable window schedule can be defined by
thermal parameters (Figure 20). Furthermore, IES<VE>™ also provides zonal CFD
analysis providing outputs of average cubic feet per minute (CFM) as a rate of outdoor
air entering the building (infiltration). Calculations are run in the Apache module and
windows are assigned opening properties using the MacroFlo module. Within MacroFlo,
glazing on external walls can be selected and adjusted to be up to 100% open.
4.4.8 Results Analysis in the Building Energy Modeling Tools
For analyzing results, the three BEM tools carried varying ranges of capabilities.
While Green Building Studio™ was able to output a comprehensive report very quickly,
the other two (Ecotect™ and IES<VE>™) provide more detailed analysis tools to help
users interrogate the results. Users requiring rapid report outputs for several areas of
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building performance may find Green Building Studio™ more favorable; meanwhile
users requiring more detailed analysis and control over how data is displayed will find
Ecotect™ and IES<VE>™ more suitable.
4.5 Guidelines for Using Building Energy Modeling
The following sections provide guidelines and recommendations for selecting and
using BEM for the analysis of high performance buildings. Section 4.5.1 provides
guidelines for utilizing BEM and provides recommendations for BEM application in
various phases of the building lifecycle. Section 4.5.2 provides potential BEM users with
guidelines for selecting the appropriate BEM tool. Intended users of the guidelines are
beginner energy modelers. These may include building designers and green building
consultants. The guidelines are based on observations made during the case study. As
such, the guidelines are tailored to the energy modeling methodology used in the
research as illustrated in Figure 4-19.
Figure 4-19. Workflow of energy modeling methodology employed in case study
Potential BEM users are encouraged to use the guidelines as a template for
developing their own energy modeling methodology and energy modeling software
criteria for evaluation and selection. Adaptations to the energy modeling methodology
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and guidelines for BEM selection presented in the following sections are necessary
based on the particular requirements and existing workflows of individual users.
4.5.1 Guidelines for Building Energy Modeling Application
The following section provides potential BEM users with guidelines on how to go
through the energy modeling process. Based on the methodology used in this research
the energy modeling process is broken down into three primary stages:
1) Develop BIM models using a gbXML-enabled BIM platform 2) Develop a baseline energy model based on ASHRAE Standard 90.1 3) Integrate energy efficiency measures for energy model optimization.
The research recommends developing BIM models in a gbXML-enabled BIM
platform. Assuming the BEM tool is interoperable with BIM via gbXML file, the amount of
model preparation time should be reduced because the building geometry does not
need to be recreated in the BEM software. Other information shared between BIM and
BEM may include glazing and building envelope constructions. Exported gbXML files
from the BIM platform should be relatively simple in order to reduce calculation times.
In Revit™, the complexity of the gbXML file export may be specified. When the
gbXML file is imported into the BEM tool, a gbXML file error check should be run to
locate and fix potential model errors that occur in the interoperation between BIM and
BEM.
While the BIM model is being developed, BEM users should also gather the
necessary information for the required inputs to develop a baseline model. Typical inputs
may include building geometry, building envelope constructions, weather file for the
closest available building location, HVAC type (refer to ASHRAE Standard 90.1 for
baseline values for building type and climate region), lighting power density per building
type, equipment power density per building type, occupancy loads and schedules.
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These inputs are entered to generate a baseline model. The outputs generated by
the baseline model serve as benchmarks against which further design iterations may be
tested in an effort to improve energy efficiency. The results of the baseline model should
be interrogated in order to identify energy uses that may be targeted to improve energy
efficiency. For example, the simulations in the case study showed that a large proportion
of energy was used for space cooling purposes. This type of energy use could then be
targeted for energy efficiency measures in order to make more significant impacts on the
overall energy consumption of the building. Energy efficiency measures that could be
implemented to reduce the cooling load include increasing the R-value of the building
envelope, integrating natural ventilation when climatic conditions are favorable, and
increasing the roof reflectance.
The final stage of the energy modeling process involves developing and testing a
series of energy efficiency measures to optimize the energy model. Various iterations of
the energy model incorporating different combinations of energy efficiency measures
can be tested against the baseline model. The percent energy savings against the
baseline model can be used to compare the different design iterations and to select the
most energy efficient combination of energy efficiency measures. These iterations may
also be used to compare models in a number of performance criteria besides energy
usage. Other performance parameters may include daylighting performance, lifecycle
cost, carbon emissions, and resource management (water and building materials). The
different iterations may also compare energy savings against initial and lifecycle costs.
Based on the various capabilities of the three BEM tools, the research identified
building lifecycle phases when these capabilities may prove useful to BEM users. The
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extent to which each BEM tool is able to supports the recommended capabilities is
indicative of how useful the BEM tool is for each respective building lifecycle phase.
Table 4-8 identifies capabilities that are useful during the conceptual design phase. All
three BEM tools appeared useful for the conceptual design phase with each tool
supporting ten out of the eleven recommended capabilities.
Table 4-8. BEM tool use during conceptual design phase BEM Capability Ecotect Green Building Studio IES<VE>Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Building geometry creation X X Orientation X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system selection X X X Design alternative assistance X Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X.
Table 4-9 identifies capabilities that are useful during the design development
phase. All three BEM tools appeared useful for design development with Ecotect™ and
IES<VE>™ supporting all ten of the recommended capabilities and Green Building
Studio™ supported nine out of the ten.
Table 4-9. BEM tool use during design development phase BEM Capability Ecotect Green Building Studio IES<VE>Energy analysis X X X Daylighting analysis X X X Natural ventilation potential X X X Passive energy potentials X X X Glazing type selection X X X Envelope constructions X X X LEED credit assistance X X X HVAC system refinement X X X Resource management X X Lifecycle cost analysis X X X
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Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X.
Table 4-10 identifies capabilities that are useful during the construction documents
phase. Ecotect™ and IES<VE>™ appeared more useful than Green Building Studio™
for this building lifecycle. Both Ecotect™ and IES<VE>™ supported four out of the five
recommended capabilities, while Green Building Studio™ only supported two.
Table 4-10. BEM tool use during construction documents phase BEM Capability Ecotect Green Building Studio IES<VE>ASHRAE Standard 90.1 compliant energy use estimate for LEED credit / code compliance
Glazing type and specifications input X X X Building envelope material selections (user-defined layers)
X X
Material schedule assistance X X Lifecycle cost analysis X X X Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X.
Table 4-11 identifies capabilities that are useful during the construction and
contracting building lifecycle phase. IES<VE>™ appeared to be the most useful BEM
tool for this building lifecycle phase supporting four out of the four recommended
capabilities. Ecotect™ was the second most useful supporting three out of the four
functions, and Green Building Studio™ was the least useful supporting one out of the
four.
Table 4-11. BEM tool use during construction and contracting phase BEM Capability Ecotect Green Building Studio IES<VE>Building material/component supplier selection
X X
Glazing supplier selection X X X Material documentation for LEED credit X X HVAC design and sizing X Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X.
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Table 4-12 identifies capabilities that are useful during the facilities management
(building operation) building lifecycle phase. IES<VE>™ appeared to be the most
useful BEM tool for this building lifecycle phase supporting five out of the five
recommended capabilities. Ecotect™ was the second most useful supporting three out
of the five functions, and Green Building Studio™ was the least useful supporting none
of the four.
Table 4-12. BEM tool use during facilities management phase BEM Capability Ecotect Green Building Studio IES<VE>Model calibration (operational profiles) X X Model calibration with plant data X Energy and cost benefits for changes to lighting systems
X X
Energy and cost benefits for changes in HVAC system operation
X
Energy and cost benefits for building envelope chagnes
X X
Inclusion of capabilities that support the specified use for each of the BEM tools is indicated by X.
Based on the capabilities provided by each BEM tool, tables 4-8 through 4-12
suggest that Ecotect™ and IES<VE>™ are useful BEM tools from conceptual design
phase to facilities management phase, while Green Building Studio™ is recommended
for use in early design stages (conceptual design and design development). Based on
Ecotect™’s capabilities, it appeared useful from the conceptual design phase through
facilities management. Green Building Studio™ appeared to be useful primarily in early
design stages (conceptual design and design development), but with limited applicability
to more detailed design stages, construction phases, and facilities management. This is
largely due to Green Building Studio™ having limited versatility in inputs and outputs.
These limitations make it very difficult to calibrate energy models. IES<VE>™’s
capabilities appeared useful for all building lifecycle phases from conceptual design to
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facilities management. By providing inputs for MEP models and actual plant data,
IES<VE>™ seemed to have increased utility during later building lifecycle phases when
compared to the other two BEM tools.
4.5.2 Guidelines for Building Energy Modeling Software Selection
The primary application of many of the BEM tools investigated in this research was
using BEM as a design tool to aid in the development of greener design iterations. As
such, the intended users of the guidelines are building designers and green building
consultants. Existing BEM tools are diverse in terms of capabilities, inputs, outputs, and
applicability to various building lifecycle phases. The following guidelines are meant to
assist potential BEM users in selecting the appropriate BEM tool for the user’s intended
BEM application. The BEM selection process includes:
1. Define the building lifecycle phases for which the BEM tool is intended to be utilized.
2. Define the required inputs as necessary to utilize the BEM for the specified building lifecycle phase applications, and use these as a checklist of pre-requisites.
3. Define the required outputs and use as a checklist of pre-requisites.
4. Rank other criteria for BEM selection (i.e. interoperability, user friendliness, and speed) in order of importance.
5. Apply appropriate weights to the criteria (based on order of importance) and score the BEM tools that meet the pre-requisites defined by steps 1 through 3.
Potential BEM users should first define the building lifecycle phases for which the
BEM tool will be utilized. Certain BEM tools are geared only towards early design stages
while others carry a wide range of capabilities and may be useful from conceptual
design to facilities management. The range of a BEM tool’s available inputs is indicative
of its applicability to various building lifecycle phases.
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Secondly, BEM users should ensure the necessary inputs are included for the
intended building lifecycle phases that the user intends to apply BEM. For instance, BEM
users intending to apply BEM to later building lifecycle phases such as facilities
management should refer to Figure 7 in section 4.1.3 Available Inputs (results for
available inputs from the initial evaluation) to make certain that the BEM tool provides
inputs for occupancy schedule, lighting schedule, equipment schedule, and plant data.
The degree of versatility of schedule implementation is particularly important. The
capability of user-defined schedules is a necessity for calibrating the energy model with
actual data obtained from building operation. Recommended required inputs for different
building lifecycle phases are illustrated in Table 4-13. These inputs may be treated as
pre-requisites to later BEM selection criteria.
Table 4-13. Recommended required inputs for BEM simulations in the different building lifecycle phases
Conceptual design Design development (in addition to those included in conceptual design)
Construction documents (in addition to those included in design development)
Construction and contracting (in addition to those included in construction documents)
Facilities Management (in addition to those included in construction documents)
Building geometry Glazing type User-defined glazing specifications
MEP model Customizable occupancy schedule
Orientation Lighting power density
User-defined envelope construction layers and properties
Water efficient fixtures
Customizable lighting schedule
Weather file Equipment power density
Customizable equipment schedule
Envelope constructions
Occupancy schedule
Plant data
Openings Lighting schedule HVAC fan power HVAC type Equipment
schedule HVAC system
levels Building type (function)
Fuel type Energy/utility rates (cost)
Operable windows Operable window
schedule
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System energy efficiency
Albedo
Thirdly, BEM users should define a set of required outputs. These may serve as
pre-requisites to later BEM selection criteria. The required outputs may differ from user
to user. After developing a checklist of required outputs, BEM users may refer to Figure
4-5 in Section 4.1.4 Available Outputs (results for available outputs from the initial
evaluation) to ensure that the potential BEM tool includes the required outputs.
After narrowing down the potential BEM tools based on the user’s required inputs
and outputs, other criteria may be integrated into the selection process. Other potential
criteria for evaluation may then be ranked in the user’s order of importance. Other
criteria, such as those used in this research, may include user friendliness,
interoperability, and calculation speed. Based on the user’s order of importance to these
criteria, appropriate weightings may be applied for scoring purposes. For example, the
most important criterion may multiply the respective score in the initial evaluation by
three; the second most important criterion may multiply the score by two; and the third
most important criterion may multiply the respective score by one. The weighted scores
may then be added together to provide a cumulative score that should indicate the most
appropriate BEM tool for the user’s specified BEM applications.
The BEM software selection process is synthesized with corresponding tables for
required inputs, (user-defined) required outputs, and examples of other “soft” criteria for
evaluation (e.g. interoperability and user friendliness) in Figure 4-20. Potential BEM
users are encouraged to use these guidelines as a template to develop their own BEM
software selection system. The criteria and subcriteria are certain to vary from user to
user. Particular users may require additional criteria and subcriteria to those used in this
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research. For example, the criterion of accuracy was not included in the scope of this
research, but may be an important criterion for potential guidelines users.
Figure 4-20. Guidelines for BEM software selection
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CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
By investigating existing BEM tools, the research provided insight on use of energy
modeling, both in terms of practice and capabilities. In practice, the integration of BEM
into building design, construction, and facilities management (still in development) will
almost certainly lead to smarter, and increasingly energy efficient buildings. However, it
remains to be seen how well these BEM tools perform for measurement purposes. Until
then, the capabilities of BEM tools are limited in application. The study recommends
such BEM tools for use primarily in design. The energy model may be used in an
iterative workflow to improve energy efficiency against a baseline model and cautions
users relying on BEM software to predict actual energy performance.
5.1 Conclusions
The following section summarizes the conclusions made during the research and
is broken down based on the initial objectives of the research.
5.1.1 Objective 1: Initial Evaluation
Based on the literature review four major criteria were identified to evaluate 12
major BEM software. These criteria were interoperability, user-friendliness, available
inputs and available outputs. Based on these four criteria for evaluation, the study
identified Autodesk Ecotect™, Autodesk Green Building Studio™, and IES<VE>™ as
the top three out of the twelve evaluated.
5.1.2 Objective 2: Case Study
The case study used the top three software (Ecotect™, Green Building Studio™,
and IES<VE>™) to compare the environmental performance of Rinker Hall (LEED Gold
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certified) and Gerson Hall (non-LEED certifiied) in three areas of environmental
performance: energy usage, dayligthing, and natural ventilation potential.
In energy usage, all three BEM tools simulated that Rinker Hall, the LEED Gold
building, would consume less energy per square foot (energy use intensity) and in total
annual energy consumption (regardless of the difference between the two buildings’
conditioned floor area).
In daylighting performance, Rinker Hall again appeared to outperform Gerson Hall
based on the selected rooms used in the case study. Although there were discrepancies
in the results between the different BEM tools used, in general Rinker Hall seemed to
provide better daylighting to these regularly occupied spaces.
Although the outputs of the three BEM tools for natural ventilation potential were
inconsistent with one another, each one simulated that Gerson Hall was better designed
to take advantage of natural ventilation than Rinker Hall. Simulation results obtained by
Ecotect™ and Green Building Studio™ showed that energy savings due to use of
natural ventilation were larger for Gerson Hall than for Rinker Hall. IES<VE>™, which
was capable of simulating airflow through spaces, predicted that Gerson Hall had higher
levels of air flow from natural ventilation. Results showed that Gerson Hall would have
higher average rates of airflow per square foot than Rinker Hall.
5.1.3 Objective 3: Re-evaluation of BEM Tools Used in the Case Study
Based on the improved and more detailed criteria for evaluation used in the re-
evaluation, the research identified IES<VE>™ as the top BEM tool when criteria are
weighted evenly. From the user specified order-of-importance matrix, it was determined
that Green Building Studio™ may be a better BEM selection for users with high priority
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on calculation speed. However, for most other criteria orders of importance, the study
recommends IES<VE>™.
5.1.4 Objective 4: Developing Guidelines for Using Building Energy Modeling
Intended users of the guidelines are building designers and green building
consultants. The guidelines were tailored to aid in BEM selection and application for
specified building lifecycle phases. Based on the required BEM capabilities for each
building lifecycle phase, it was evident that many of the BEM tools investigated in the
study are appropriate for early design stages, while only a few (IES<VE>™, Ecotect™,
and eQuest™) may be useful for later design phases, construction and contracting, and
facilities management.
5.2 Research Limitations
As an evaluation of existing BEM tools, the research sought to develop a
methodology that compared these tools in a relatively consistent manner. This proved to
be very difficult as the existing BEM tools are very diverse with different intended users
and applications. Thus, while the research attempted to develop criteria for evaluation
that could fairly compare such diverse programs, these criteria are almost certainly
tailored to a preconceived notion of BEM while the project was still in its developmental
stage.
5.2.1 Objective 1: Initial Evaluation
The initial cross evaluation relied on information gathered during the literature
review to fill out sub-criteria checklists for each criterion for evaluation. The data was
limited to available data and literature to fill out these checklists. Ideally, the study would
have test driven each of the 12 BEM tools used in this portion of the study but was
limited by time and software costs. This portion of the study also assumed an even
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weight applied to each criterion for evaluation. In order to select the top three BEM tools
out of the 12 investigated, the study was limited at this portion to an even-weighted
scoring system.
5.2.2 Objective 2: Case Study
In the development of any energy model, a number of assumptions must be made.
The number of variables that affect building energy usage are vast, so the model is
reliant on a number of assumptions and conditions. These assumptions also varied from
program to program based on the available inputs provided for each one.
In all scenarios, the implementation of schedules is always an approximation as it
is impossible to predict the actual behavior of occupants and building operation
practices. Ecotect and IES<VE>™ have capabilities of implementing increasingly
accurate schedules that could be customized on a zone-by-zone basis. Meanwhile,
generalized assumptions were made in Green Building Studio™ about occupancy and
operation based on default values and averages for schedules for higher education
building types. Similarly, values for lighting power density and equipment power density
were based on standard and averaged values per building type based on the ASHRAE
90.1 Standard (this was applied to all three BEM tools). These values along with
corresponding schedules simulate approximations in regards to HVAC use and internal
gains.
Regarding daylighting performance (as per LEED requirements) CIE uniform sky
conditions for simulation purposes were assumed in all three BEM tools. Ecotect™’s
daylighting calculations were limited to only taking daylight factor data for December 21
(worst case scenario), while IES<VE>™’s daylighting simulations were limited to
September 21 (average case scenario). Green Building Studio™’s daylighting simulation
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methodology is uncertain as all inputs and settings related to daylighting (except for
glazing specifications) are automated. Because of these default and inconsistent
daylighting simulation settings among the three BEM tools used in the case study, the
research was limited to comparing the daylighting performance between the two
buildings for each BEM tool individually, and could not compare the results between the
different BEM tools. As previously mentioned, the study would have ideally compared
the daylighting performance in terms of daylight autonomy instead of daylight factor.
This calculation is preferred by the AEC community in that it accounts for daylighting
performance throughout the year and describes daylighting as the percentage of time
that spaces do not have to rely on electrical lighting. Daylight factor can be taken at any
time leading to inconsistent simulation practices throughout the industry. These
inconsistencies are illustrated by the limitations of the three software, each of which
calculate daylight factor at different times. Due to these limitations, the research was
only able to assess daylight performance in terms of daylight factor.
Similarly in the natural ventilation simulations, no uniform simulation methodology
could be established among the three BEM tools. This again limited the research to
comparing the performance of the two buildings within each BEM tool individually. Green
Building Studio™’s natural ventilation simulation was limited to default settings and
values. In developing the operational profile for operable windows in Ecotect™, the
research had to rely on weather data and input operational values manually. The
operational schedule used assumes that operable windows are fully open during days
when the outdoor temperature is within the ASHRAE Standard 55 comfort range. A
similar assumption was made in the operational profile for operable windows in
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IES<VE>™, which used a thermal parameter formula to trigger operable windows to be
100% open when the outdoor temperature is between 70°F and 78°F. In all three BEM
tools, the following assumptions were made:
• Operable windows are 100% open during times of acceptable outdoor temperature
• The buildings are designed to allow for the stack effect and/or cross-ventilation to occur
• All windows are operable
5.2.3 Objective 3: Re-evaluation of the BEM Tools Used in Case Study
The re-evaluation portion of the study opted to update the set of criteria for
evaluation based on the observations from the case study. The categories of available
inputs and available outputs were combined into a single criterion, versatility. The sub-
criteria within versatility are also broken down to assess the amount of inputs and
outputs supported by each BEM tool, as well as the degree of resolution within each
one. Speed was also added as another criterion in the re-evaluation as it was
discovered that the time required for certain programs performing certain calculations
was a major disadvantage to the software. Ideally, the criteria for evaluation used in the
re-evaluation would also have been used in the initial evaluation phase of the research.
5.2.4 Objective 4: Developing Guidelines for Using Building Energy Modeling
One of the major difficulties and limitations in developing the set of guidelines in
this research was the fact that the observations made during the project (as summarized
in Appendix B) were only based on the energy modeling methodology forged by a single
user both learning and using these BEM tools for the first time. Many of the advantages,
disadvantages, and complications associated with the three BEM tools were based on
subjective observations and BEM use (e.g. other users of the software may not run into
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the same problems, or discover other problems, etc). The guidelines presented in the
research are thus based on a single energy modeling methodology and workflow.
5.3 Recommendations for Future Research
As Krygiel and Nies (2008) note, the two primary ways in which BEM tools are
utilized are for design and for measurement. While this research can remark on the
applicability of BEM as a design tool and for meeting simulation-based LEED credit
requirements, the accuracy of these BEM tools remains to be assessed. In that regard,
these tools are limited to acting only as design tools and for the sole function of
improving environmental performance. Future research assessing the accuracy of these
BEM tools, particularly those used in the case study, could be useful to provide
recommendations to software developers, and could potentially improve the faith in BEM
users that buildings will meet intended performance requirements. In particular, future
research could focus on measuring simulated energy usage against measured data for
each of the two buildings used in the case study and compare energy use breakdowns.
System levels and operational profiles (schedules) can be adjusted to calibrate the
energy models with actual building operation.
Another objective of future research could be a comparison of gbXML file-based
energy models and IFC file-based energy models. As several model errors were
discovered in the interoperability between Revit™ and the BEM tools via gbXML file
import/export, it would be useful to BEM users to gain insight into which data schema
contains less model errors.
A couple of changes in the research methodology would be made if the study were
to be conducted again. For one, the more comprehensive criteria for evaluation used in
the re-evaluation would also be applied to the initial evaluation. As the research
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progressed through the case study phase, the criteria for evaluation became more
refined. Secondly, the study would have compared the daylighting performance for all
regularly occupied spaces of the two buildings as opposed to selected rooms. In this
way, a more accurate and comprehensive comparison of the daylighting performance of
the two buildings could be made. Finally, the criterion of accuracy should be added to
the re-evaluation of the BEM tools. The objective of the future research will be to
validate the accuracy of the BEM tools. An additional study comparing the data of
simulated energy usage against measured data for the two buildings used in the case
study is recommended for future research. The percent differences between simulated
data and measured data could serve as the basis for scoring the BEM tools in the
accuracy criterion, and these scores can be added to those in the re-evaluation as an
additional criterion for evaluation.
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109
APPENDIX A
INITIAL EVALUATION
Table A-1. lnteroperability subcriteria checklist and raw scores
110
Table A-2. User friendliness sub-criteria checklist and raw scores
111
Table A-3. Available inputs subcriteria checklist and raw scores
112
Table A-3. Continued
113
Table A-4. Available outputs checklist and raw scores
114
115
Table A-4. Continued
Table A-5. Cumulative score with respective criteria scores
APPENDIX B CASE STUDY
116
Table B-1. Annual Energy Usage Rinker Hall (output of Green Building Studio simulation)
Energy, Carbon and Cost Summary Annual Energy Cost $90,956 Lifecycle Cost $1,238,824
Annual CO2 Emissions Electric 453.7 tons Onsite Fuel 49.9 tons Large SUV Equivalent 45.8 SUVs / Year
Annual Energy Energy Use Intensity (EUI) 59 kBtu / ft² / year Electric 687,488 kWh Fuel 8,601 Therms Annual Peak Demand 215.3 kW
Lifecycle Energy Electric 20,624,649 kW Fuel 258,015 Therms
Figure B-1. Rinker Hall energy use breakdown (output of Green Building Studio
simulation)
117
Figure B-2. Rinker Hall annual fuel use breakdown (output of Green Building Studio
simulation)
Table B-2. Annual Energy Usage Gerson Hall (output of Green Building Studio
simulation) Energy, Carbon and Cost Summary
Annual Energy Cost $87,013 Lifecycle Cost $1,185,112
Annual CO2 Emissions Electric 440.7 tons Onsite Fuel 43.2 tons Large SUV Equivalent 44.0 SUVs / Year
Annual Energy Energy Use Intensity (EUI) 78 kBtu / ft² / year Electric 667,753 kWh Fuel 7,443 Therms Annual Peak Demand 218.9 kW
Lifecycle Energy Electric 20,032,602 kW Fuel 223,282 Therms
118
Figure B-3. Gerson Hall energy use breakdown (output of Green Building Studio
simulation)
Figure B-4. Gerson Hall Energy Use Breakdown (output of Green Building Studio
simulation)
119
Table B-3. Natural Ventilation Gains Rinker Hall (output of Ecotect simulation)
120
Table B-4. Natural Ventilation Gains Gerson Hall (output of Ecotect simulation)
121
Table B-4. Continued
Table B-5. Natural Ventilation Potential Rinker Hall (output of Green Building Studio
simulation) Natural Ventilation Potential
Total Hours Mechanical Cooling Required: 6,230 Hours Possible Natural Ventilation Hours: 1,370 Hours Possible Annual Electric Energy Savings: 32,254 kWh Possible Annual Electric Cost Savings: $3,677 Net Hours Mechanical Cooling Required: 4,860 Hours Table B-6. Natural Ventilation Potential Gerson Hall (Output of Green Building Studio
simulation) Natural Ventilation Potential
Total Hours Mechanical Cooling Required: 4,872 Hours Possible Natural Ventilation Hours: 1,000 Hours Possible Annual Electric Energy Savings: 50,645 kWh Possible Annual Electric Cost Savings: $5,774 Net Hours Mechanical Cooling Required: 3,872 Hours
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Table B-7. Natural Ventilation Airflow Rinker Hall (output of IES<VE> simulation) Rinker Hall Room Designation Sq. Ft. avg. CFM 30 mech. 1608.5 44.1 30A elec. 301 8.7 106 Medium Classroom 955 24.4 110 Large classroom 1803 49.5 110A Elec 73 2.2 115 Student lounge 500 12.8 125 MEP Studio 1670 44.9 134 shower 85 1.4 136 shop 296 8.7 138 soils/conc. 734 18.4 140 structures studio 1331 35.9 140A Stroage 436 13.8 141 Interview 110 2.4 143 Interview 108 2.2 145 Men 235 5.3 146 Women 277.5 5.4 146A Mech Room 73 2.2 201 Tech 244.5 5.4 202 DES 586 14.5 203A Server Room 147 3.3 204 Plan Room 214 4.2 205 Janitor 29 0.8 206 Computer Lab 1191 29.2 207A Storage 76 1.4 208 Information Tech. 438 9.9 209 MCE 144 4.4 210 Medium Classroom 889 22.8 215 Medium Classroom 914 23.2 220 Medium Classroom 903 21.9 225 Medium Classroom 907 23 230 Medium Classroom 1006 25.1 235 Storage 236 6.3 235A Elec 32 0.5 238 Construction 1248 30.7 240 Est/Dwg/Sch 1437 34.6 245 Men 208 4.7 245A Storage 25 0.5 246 Women 256 5.8 246A Storage 23 0.5 301 Admin 170.5 3.7 302 Director Grad. 259 5.9 303 Main Conference Room 656 16.7 305 Main Office 998 22.5
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Table B-7. Continued Rinker Hall Room Designation Sq. Ft. avg. CFM 305A Office Mgr 142 3.1 306 Director 283 6.2 307 Associate Director 257 5.2 308 Storage 167 3.4 309 Faculty Office 153 3 310 Mail/Kit/Copy 255 5.1 311 Faculty Office 152 3 312 Conference Room 274 5.6 313 Resource Center 167 3.4 314 Faculty Office 153 3 315 Faculty Office 152 3 316 Faculty Office 153 3 319 Faculty Office 152 3 320 Grad Studio 363 7.5 321 Faculty Office 153 3 322 Faculty Office 152 3 323 Faculty Office 153 3 324 Grad Studio 317 6.6 325 Faculty Office 152 3 326 Grad Studio 317 6.6 327 Faculty Office 153 3 328 Grad Studio 317 6.6 329 Faculty Office 152 3 331 Faculty Office 153 3 332 Faculty Office 175.5 3.2 333 Closet 22 0.5 336 BCIAC 533 12.5 336B Elect Closet 31 0.6 338 CPR 593 15.1 340 CCE 555 13.1 341 CCSLC 596.5 14.8 342 Endowed Chair 164 3.6 343 Storage 75 2.3 344 E-Journal Editor 344 3.8 345 Men 225 4.8 345A Janitor 52 1.5 346 Women 269 6.2 C199D Corridor 4032 25.4 C299D Corridor 4029 57.7
124
Table B-8. Natural Ventilation Airflow Gerson Hall (output of IES<VE> simulation) Gerson Hall Room Designation Sq. Ft. avg. CFM 103 Elec 22.6 0.5 104 Janitor 32.5 0.8 105 Men 205 6.4 107 Women 221 6.9 108 Data/Comm 156 4.9 112 mechanical 516 17.2 112B Elec. 114 4 112 C Fire Pump 119 3.6 114 MACC Services 132 4.5 115 Gallery 244 8.1 116 Student Office 390 11.4 121 Medium Classroom 1327 43.9 122 Medium Classroom 1275 43.2 124 Control Room 136 4.3 125 Teaching Assistants 563.4 20 126 Large Classroom 2602 131 127 Men 264.5 8.8 128 Women 245.75 7.5 204 Janitor 37.6 0.7 205 Men 206 5.4 207 Women 209 5.3 208 Data/Comm 87.82 2.1 211 Mail 144.71 4.6 212 Clerk 85.3 2.7 213 Clerk 85.3 2.7 214 Stor/Admin Support 104.94 3.3 215 Small Conference 218.2 6 216 Director / Chair 232 7 217 Asst Dir Dept Chair 172 5.5 218 Gen Staff 111.4 3.2 219 Coord 111.4 3.2 221 Work Room 232 7 227 M Acc Reading Room 750 22.5 228 Small Classroom 808.6 24.6 229 Small Classroom 808.2 24.4 230 Break - Out 106 3.3 231 Break - Out 166 4.8 232 Break - Out 119.4 3.5 233 Break - Out 119.4 3.5 234 Break - Out 119.4 3.5 235 Break - Out 170.4 4.8 236 Break - Out 105.12 3.3 237 Break - Out 112 3.5 238 Break - Out 112 3.5 240 Storage 98 2.1 303 Elec 12.4 0.2 304 Janitor 24.8 0.4 305 Men 206 6.1 307 Women 209.4 6 308 Data / Comm 88 2.4 309 Office 167 4.7 310 Office 182 5.3
125
Table B-8. Continued Gerson Hall Room Designation Sq. Ft. avg. CFM 311 Office 177 5.5 312 Office 177 5.5 314 Office 177 5.5 315 Office 181.6 5.3 316 Office 223 6.6 318 PhD Office 180 5.1 319 Office 160.5 5 320 Office 166 5.2 321 Office 170.4 5.3 322 Office 160.5 5.1 324 Office 160.5 5 325 Office 160.5 5 326 Conf Rm Support 119.6 3.7 327 Large Conference Room 841.8 26.6 328 Faculty Reading / Lounge 389.2 11.8 328A Faculty Support 93 3.1 329 PhD Office 293 9.5 330 Office 152 4.8 331 Office 154.5 4.9 332 Office 154.5 4.9 333 Office 154 4.9 334 Ph D Office 365 11.5 335A Stor 39 1 336 Office 196.3 5.7 337 Office 202 5.5 338 Office 191 5.7 339 Office 184.5 5.5 340 Office 197 5.7 C199A Commons Area 3052 109.1 C199C Corridor 254 8.4 C199G Entry/Corridor 1491.64 52.2 C199H Corridor 272.77 6.8 C199J Corridor 486 16.6 C299A Corridor 567 14.9 C299B Corridor 958.32 31.7 C299C Corridor 495 14.5 C299F1 Corridor 807.32 21.6 C299F Corridor 1361 43.5 C299G Corridor 384.5 9.2 C399A Corridor 814.16 26.2 C399C Corridor 902.33 28.4 C399D Corridor 990 29.9
126
127
APPENDIX C GUIDELINES FOR USING BUILDING ENERGY MODELING
Table C-1. Ecotect™ Guidelines and Recommendations Matrix
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Table C-2. Green Building Studio™ Guidelines and Recommendations Matrix
129
130
Table C-3. IES<VE>™ Guidelines and Recommendations Matrix
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BIOGRAPHICAL SKETCH
Thomas (“TJ”) Reeves received his Bachelor of Architecture at Syracuse
University School of Architecture in 2009 and Master of Science in Building Construction
with a concentration in sustainability at the University of Florida M.E. Rinker, Sr. School
of Building Construction in 2012. He is a co-founder of the design firm Lusona Design
with built work in the Philippines, and a project underway in Los Angeles.
His interests in art, science and culture have led him to a passion for the built
environment. In seeking a master’s degree in building construction in addition to a
bachelor’s degree in architecture, he seeks to bridge the divide between designer and
builder. Whether as designer, builder, or researcher, he sees the production of the built
environment simply as craft.