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NITED STAT S OFA ERICA

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Research Toward Zero Energy Homes August 2006 • NREL/TP-550-39929

BEoptTM Software for Building Energy Optimization: 

Features and Capabilities

Craig Christensen and Ren Anderson

National Renewable Energy Laboratory

Scott Horowitz, Adam Courtney, and Justin Spencer

University of Colorado, Boulder
BEoptTM Software for Building 

Energy Optimization: Features 

and Capabilities 

C. Christensen and R. Anderson 

National Renewable Energy Laboratory 

S. Horowitz, A. Courtney, and J. Spencer 

University of Colorado, Boulder 

Prepared under Task No. BET6.8004 

Technical Report 

NREL/TP-550-39929 

August 2006 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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BEoptTM Software for Building Energy Optimization: 

Features and Capabilities 

Abstract 

A zero net energy (ZNE) building produces 

as much energy as it uses on an annual basis, 

using a grid-tied, net-metered photovoltaic 

(PV) system and active solar. The optimal 

path to ZNE extends from a base case to the 

ZNE building through a series of energysaving building designs with minimal 

energy-related costs. 

The BEoptTM software is designed to find 

optimal building designs along the path to 

ZNE. A user selects from predefined options 

in various categories to specify options to be 

considered in the optimization. Energy 

savings are calculated relative to a reference. 

The reference can be either a user-defined 

base-case building or a climate-specific 

Building America Benchmark building 

automatically generated by the BEopt 

software. The user can also review and 

modify detailed information on all available 

options in a linked options library 

spreadsheet. 

The BEopt software calls the DOE2 and 

TRNSYS simulation engines and uses a 

sequential search technique to automate the 

process of identifying optimal building 

designs along the path to ZNE. The BEopt 

software finds these optimal and nearoptimal designs based on discrete building 

options reflecting realistic construction 

options. The BEopt software handles special 

situations with positive or negative 

interactions between options in different 

categories. 

The BEopt software includes a results 

browser that allows the user to navigate 

among different design points and retrieve 

detailed results regarding energy end-use 

and option costs in different categories. 

Multiple cases, based on a selected 

parameter such as climate, can be included 

in a BEopt project file for comparative 

purposes. 

1. Background 

1.1 Types of Zero Energy Buildings 

Historically, fully autonomous zero energy 

buildings have been built independent of any 

connection to the utility grid. In a new 

approach, the zero net energy building 

promises more widespread applicability. 

The ZNE building uses grid-tied, netmetered PV and active solar to produce as 

much energy as it uses on an annual sourceenergy basis. 

1.2 Source versus Site Energy 

Accounting 

ZNE can be defined in terms of site energy 

(used at the building site) or source energy 

(sometimes called primary energy). Source 

energy provides a metric for assessing total 

energy use when dealing with multiple fuel 

types. From a societal point of view, source 

energy better reflects the overall 

consequences of energy use and is 

appropriate for ZNE buildings analysis. 

Source-to-site energy ratios depend 

somewhat on location and the specifics of 

what is included in the calculation. The user 

can enter specific values into the BEopt 

software. For electricity purchased from a 

utility, source energy accounts for powerplant generation efficiency and transmission 

1 
and distribution losses, and site-to-source 

ratios are typically about 3, depending on 

the mix of electrical generation types (coalfired, natural-gas combined-cycle, nuclear, 

hydropower, etc.) For natural gas and 

propane, the source-to-site energy ratios are 

slightly greater than 1. 

2. The Path to Zero Net Energy 

Energy and cost results can be plotted in 

terms of annual energy-related costs (the 

sum of utility bills and mortgage payments 

for energy options) versus percent energy 

savings (Figure 1). The path to zero net 

energy extends from a base case (e.g., a 

current-practice building, a code-compliant 

building, or some other reference building) 

to a ZNE building with 100% energy 

savings. The optimal path is defined as the 

lower bound of results from all possible 

building designs (i.e., connecting minimal 

cost points for various levels of energy 

savings). Alternatively, net present value or 

other economic figures of merit could be 

shown on the y-axis. 

Points of particular significance on the path 

(Figure 1) can be described as follows. From 

the base case at point 1, energy use is 

reduced by employing building efficiency 

options (e.g., improvements in wall R-value, 

furnace AFUE, air conditioner SEER, etc.) 

A minimum annual cost optimum occurs at 

point 2 (assuming the minimum does not 

occur at the base case). Additional building 

efficiency options are employed until the 

marginal cost of saved energy for these 

options equals the cost of producing PV 

energy at point 3. From that point on, energy 

savings are solely a result of adding PV 

capacity until ZNE is achieved at point 4. 

0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Mortgage + Utilities ($/year)

mortgage

utility bills

cash flow

1

2

3

4

Fig. 1: Conceptual plot of the path to ZNE.

0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Mortgage + Utilities ($/year)

mortgage

utility bills

cash flow

1

2

3

4

Fig. 1: Conceptual plot of the path to ZNE. Fig. 1: Conceptual plot of the path to ZNE 

2 
 3. Building Energy Optimization 

Building energy simulations are often used 

for trial-and-error evaluation of “what-if” 

options in building design (i.e., a limited 

search for an optimal solution). In some 

cases, a more extensive set of options is 

evaluated and a more methodical approach 

is used. For example, in the Pacific Gas and 

Electric ACT2

 project, energy efficiency 

measures were evaluated using DOE2 

simulations in a sequential analysis method 

that explicitly accounted for interactions [1]. 

With today’s computer power, the 

bottleneck is no longer run times for 

individual simulations, but rather the human 

time to handle input/output. Computerized 

option analysis has the potential to automate 

the input/output, evaluate many options, and 

perform enough simulations to explicitly 

account for the effects of interactions among 

combinations of options. However, the 

number of simulations still needs to be kept 

reasonable, by using a search technique 

rather than attempting exhaustive 

enumeration of all combinations of options. 

Even with simulations that run in a few 

seconds, exhaustive enumeration run time is 

prohibitive for the millions of combinations 

that can result from options in, for example, 

ten or more categories. 

Recently, several computer programs have 

been developed to automate building energy 

optimization. For example, EnergyGaugePro uses successive, incremental 

optimization (similar to the ACT2

 approach) 

with calculations based on the “energy code 

multiplier method” for Florida [2]. GenOpt 

is a generic optimization program for use 

with various building energy simulation 

programs and user-selectable optimization 

methods [3]. 

3.1 Constrained versus Global 

Optimization 

From a purely economic point of view, 

building energy optimization involves 

finding the global optimum (the minimum 

annual cost) that balances investments in 

efficiency versus utility bill savings. 

However, there are sometimes noneconomic reasons for targeting a particular 

level of energy savings. Given a particular 

energy-savings target, economic 

optimization can be used to determine the 

optimal design (lowest cost) to achieve the 

goal. This sort of constrained optimization 

can also apply for other target levels of 

energy savings between the base case and 

ZNE and is the basis for establishing the 

optimal path to zero net energy. 

3.2 Discrete versus Continuous 

Variables 

In theory, optimal values can be found for 

continuous building parameters. In the 

practice of designing real buildings, 

however, the process often involves 

choosing among discrete options in various 

categories. For example, options in the wall 

construction category may include 2x4 R11, 

2x4 R13, 2x6 R19, 2x6 R19 with 1-in. foam, 

2x6 R19 with 2-in. foam, etc. 

If discrete option characteristics for a 

particular category fall along a smooth 

curve, a continuous function could be used 

to represent that category in an optimization 

methodology (along with other discrete and 

continuous categories). After optimization, 

the discrete options closest to the optimal 

values could be selected. However, the 

resulting combination of options may not 

necessarily be truly optimal, because when 

the option nearest (but not equal) to the 

optimal value in one category is selected, the 

optimal values for other categories may 

change. 

3 
Even if energy use as a function of a 

particular building parameter is well 

behaved, the introduction of costs (e.g., for 

particular wall construction options) may 

introduce significant irregularities. In fact, 

given the discrete products available in 

many categories (wall construction, glass 

type, air conditioners, furnaces, etc.), a 

smooth, continuous energy/cost function 

occurs in relatively few cases (e.g., loose-fill 

ceiling insulation). In general, if discrete 

options are to be considered, they should be 

dealt with as such. 

3.3 Near-Optimal Solutions 

It is advantageous for the optimization 

methodology to present multiple solutions 

(optimal and near-optimal). Near-optimal 

solutions achieve ZNE or a particular level 

of energy savings with total costs close to 

the optimal solution total cost. Given 

uncertainty in cost assumptions and energyuse predictions, near-optimal points may be 

as good as optimal points. For various nonenergy/cost reasons, the alternative 

construction options in near-optimal 

solutions may be of interest to building 

designers. Some such solutions can be 

identified by the optimization search 

technique, while others can be added with 

perturbation techniques. 

4. BEopt Software 

The BEopt software is a computer program 

designed to find optimal building designs 

along the path to ZNE and to accelerate the 

process of developing high-performance 

building designs. In addition to an 

optimization search, the BEopt software 

includes (1) a main input screen that allows 

the user to select, from many predefined 

options, those to be used in the optimization, 

(2) an output screen that allows the user to 

display detailed results for many optimal 

and near-optimal building designs, and (3) 

an options library spreadsheet that allows a 

user to review and modify detailed 

information on all available options. These 

BEopt interface components (inputs, library, 

and outputs) are the subject of this paper. 

The main input screen allows a user to select 

from predefined options in various 

categories (e.g., wall type, ceiling type, 

window glass type, HVAC type, etc.) to 

specify options to be considered in the 

optimization. The user can also review and 

modify detailed information on all available 

options and the Building America 

Benchmark [4] in a linked options library 

spreadsheet. 

The BEopt software calls the DOE2 [5] and 

TRNSYS [6] simulation engines. TMY2 

DOE2

TRNSYS

SDHW

PV

TMY2

Heating

Cooling

Lighting

Appliances

Optimal

ZNE

Designs

BEopt

Fig. 2: Optimization with multiple simulation programs.

DOE2

TRNSYS

SDHW

PV

TMY2

Heating

Cooling

Lighting

Appliances

Optimal

ZNE

Designs

BEopt

DOE2

TRNSYS

SDHW

PV

TMY2

Heating

Cooling

Lighting

Appliances

Optimal

ZNE

Designs

BEopt

Fig. 2: Optimization with multiple simulation programs. Fig. 2: Optimization with multiple simulation programs 

4 
weather data [7] are used for all simulations 

(Figure 2). 

The DOE-2 simulation program is used to 

calculate energy use as a function of 

building-envelope options and HVAC 

equipment options. Appliance and lighting 

option energy savings are calculated based 

on energy-use intensity factors and 

schedules input into DOE2. The TRNSYS 

simulation program is used to calculate 

water-heating loads and energy savings for 

solar water heating. TRNSYS is also used to 

calculate annual electrical energy production 

from a grid-tied PV system. The PV array is 

modeled using the approach developed by 

Sandia National Laboratories [8] and the 

database of performance characteristics 

published on its Web site 

(www.sandia.gov/pv/pvc.htm). Perfect 

maximum power-point tracking is assumed. 

The inverter efficiency is assumed to follow 

the shape of a Trace SW series inverter, with 

a capacity of 1.2 times the rated PV array 

output at standard rating conditions. A 

prototype version of the BEopt software 

using EnergyPlus [9] has demonstrated 

technical feasibility for future use of a single 

simulation engine with extended 

capabilities. 

The BEopt software automates the process 

of identifying optimal building designs 

along the path to ZNE using a sequential 

search technique [10]. At each step along the 

path, the BEopt software runs a series of 

simulations incorporating each user-selected 

option one at a time and searches for the 

most cost-effective combination of options. 

The sequential search technique has several 

advantages. First, it finds intermediate 

optimal points along the entire path (i.e., 

minimum-cost building designs at different 

target energy-savings levels, not just the 

global optimum or the ZNE optimum). 

Second, discrete rather than continuous

building options are evaluated, providing 

realistic construction options. Third, 

multiple near-optimal designs are identified 

at each particular energy-savings level, 

providing design alternatives. 

The BEopt software has the ability to handle 

special situations caused by interactions 

between options. Special situations with 

negative interactions are handled by looking 

back along the path and continually reevaluating previously rejected options (to 

properly identify the potential of largesavings options and options involving tradeoffs between categories). Positive 

interactions between categories are 

accommodated by allowing the user to 

define combined options in the BEopt 

software (to ensure that such potentially 

synergistic combinations are evaluated 

during optimization). 

Multiple user-defined cases can be included 

in a BEopt project file. Multiple cases are 

often used to analyze building performance 

as a function of climate. Cases can also be 

used to study how building performance is 

affected by economic parameters, PVsystem characteristics, or the options 

selected for optimization. Up to 20 cases can 

be defined, with case tabs displayed along 

the bottom of the screen. Once an 

optimization has been completed, each case 

contains input screens and an output screen. 

The main output screen includes a results 

browser that allows the user to navigate 

among the results associated with each 

(optimal and non-optimal) building design 

simulated during optimization. For each 

building design, the browser will display 

detailed results regarding energy 

consumption, costs, and options. If multiple 

cases exist in a project file, a Combined 

Graphs output screen will be available. 

5. Options Library 

The options library spreadsheet (Figure 3) 

contains detailed information for all the 

5 
BEopt categories and options. The options 

currently in the library are intended to 

represent readily available products and 

construction techniques. Whereas some 

users may only use the BEopt input screens 

to select from the currently available 

options, access to the options library allows 

the user to review and modify specific 

characteristics of each option such as energy 

properties and cost assumptions. 

Each category worksheet lists the option 

numbers across the top of the screen. The 

Building America Benchmark is listed 

before the available options. The details of 

each category are grouped under three 

subtitles (General, Energy, and Economics). 

In addition to constants input values, the 

spreadsheet includes many formulas. Option 

characteristics are converted to simulation 

input values. Climate-specific building 

characteristics are calculated for the 

Building America benchmark. Values in 

bold-faced type are linked to a summary 

worksheet and used in the simulation input 

files. Additional worksheets in the file allow 

access to the DOE2 and TRNSYS input files 

for more advanced users. 

Each option has an assumed first cost and 

lifetime costs. Costs are retail and include 

national average estimated costs for 

hardware, installation labor, overhead, and 

profit. Some are input as unit costs that are 

Fig. 3: Options library spreadsheet 

6 
then multiplied by a category constant (e.g., 

ceiling insulation costs are input per square 

foot and automatically multiplied by ceiling 

area). Some inputs are energy-option 

specific (e.g., the cost of solar water-heating 

systems). Inputs can also be based on total 

costs (e.g., the cost of wall constructions 

with different insulation values) because the 

analysis method will calculate the 

differences between option costs. 

Construction costs (wall insulation, ceiling 

insulation, foundation insulation, etc.) are 

user specified or based on national average 

cost data from sources such as R.S. Means 

[11] or California Database for Energy 

Efficient Resources [12]. Window and 

HVAC costs are based on quotes from 

manufacturers’ distributors. Appliance costs 

are based on manufacturers’ suggested retail 

prices. In the future, the user will be able to 

select from amongst different cost 

assumptions for each option stored in a 

database. 

Lifetimes for building construction options 

(wall insulation, ceiling insulation, 

foundation insulation, windows, etc.) are 

assumed to be 30 years. Equipment and 

appliance options typically have 10- or 15-

year lifetimes. Lifetimes for lighting options 

(incandescent and compact fluorescent 

lamps) are modeled based on cumulative 

hours of use. 

The BEopt Cost Combination Editor (Figure 

4) can be used to develop user datasets of 

selected option costs (and lifetimes). 

Fig. 4: Option Cost Combinations Editor screen

7 
6. Input Screens 

6.1 Building Geometry 

Some building geometry parameters (e.g., 

floor area, number of stories, roof 

characteristics, garage characteristics, etc.) 

are input via a dialog box (Figure 5). Other 

building geometry parameters (e.g., 

orientation, aspect ratio, window area per 

wall, eaves, and distance to neighboring 

buildings) are input as options available for 

optimization. House geometries based on 

BEopt user inputs can be rendered and 

displayed in either eQUEST [13] or 

SketchUp [14] (as shown in Figure 6, with 

neighboring houses for shading analysis). 

6.2 Photovoltaic System Parameters 

Some photovoltaic system parameters (e.g., 

module type, cost, de-rate factor, etc.) are 

input via a dialog box (Figure 5). Other 

photovoltaic system parameters (e.g., tilt, 

azimuth, and size) are input as options and 

are available for optimization. Tilt options 

can be specified relative to roof pitch or as 

absolute values. Azimuth options can be 

specified relative to house orientation or as 

absolute values. Size options can be 

specified relative to roof area or as absolute 

values. 

Fig. 5: Building and photovoltaic system input screen 

Fig. 6: Sample of house geometry (with neighboring houses for shading analysis)

 based on user inputs in BEopt 

8 
6.3 Economic Parameters 

Economic parameters (e.g., analysis period, 

inflation rate, discount rate, mortgage 

period, mortgage rate, down payment, 

income tax rate, utility rates, etc.) are input 

via a dialog box (Figure 7). Gas and electric 

utility rates can be selected as either national 

averages, state averages, or user specified 

with fixed- and marginal-cost components. 

6.4 Energy-Saving Options 

Upon opening a project file, the BEopt 

software scans the options library 

spreadsheet and loads categories and options 

associated with the project (Figure 8). 

Option categories are divided into eight 

groups: Building, Envelope, Windows & 

Shading, Appliances & Lighting, 

Equipment, Renewables, Include 

Combinations, and Exclude Combinations. 

Selecting a category causes the options in 

that category to be listed in the display 

window. The user can then select options in 

each category to be included in the 

optimization. The summary matrix on the 

left side of the screen shows currently 

selected options by option number, and the 

estimated number of simulations and run 

time for the optimization are displayed. The 

radio button that is selected and highlighted 

in red designates the option included for the 

user-specified base case. Detailed 

information on all options can be viewed 

and modified by selecting the magnifyingglass button located at the upper-right corner 

of the options box. This will open the 

options library (described earlier). 

Fig. 7: Economics input screen 

A user may want to select only a single 

option in a particular category to specify a 

building characteristic. For example, if the 

building site dictates the building orientation 

as east, only the east option needs to be 

selected. However, if various orientations 

are possible, multiple options could be 

selected for the BEopt software to consider 

during optimization. 

In other situations, subsets of options may 

be mutually exclusive. In the Foundation 

category, for example, options exist for slab9 
on-grade, crawl space, and basement 

configurations. 

If slab-on-grade is the desired foundation 

type, only options of this type should be 

selected. Ultimately, the user chooses 

whether subsets of options should be treated 

as mutually exclusive or not. For example, 

should the optimization include all spaceheating and water-heating equipment 

options (both gas and electric) or should 

only electric options or only gas options be 

considered? Performance characteristics of 

different options may also determine their 

selection (or de-selection). For instance, 

glazing types with low solar heat gain 

coefficients (SHGCs) may not be desired in 

a climate where passive solar heating is 

desirable. 

Finally, the user is provided the ability to 

explicitly include or exclude certain 

combinations of options for evaluation. For 

example, if the user is interested in the 

interaction between south-facing glazing 

area and additional thermal mass, simply 

selecting available options within these two 

categories may not suffice. This is caused by 

the nature of the sequential search 

optimization technique – an option from 

either the thermal mass category or the 

glazing area category must first be 

independently cost-effective in order for the 

combination of additional thermal mass and 

glazing area to be evaluated by the BEopt 

software. However, if the user has the 

technical experience to recognize this 

situation, an Include Combinations category 

(for example, “Glass+Mass”) can be created 

Fig. 8: Input options selection screen 

10 
in which these combinations are explicitly 

selected for the optimization. Likewise, the 

user can create Exclude Combinations 

categories such that certain combinations are 

explicitly excluded from the optimization – 

usually as a way of reducing runtime for 

combinations that the user knows are not 

cost-effective. 

With the options selected, the BEopt 

software can run an optimization of the 

possible combinations of options. For 

selected options similar to those in Figure 5, 

optimization would involve approximately 

1000 simulations and approximately 1.5 

hours of computer run-time. 

7. Output Screen (Results Browser) 

Once an optimization process has been run, 

the output screen can be displayed. Figure 9 

illustrates a typical example of the output 

screen. For each case, the BEopt output 

screen displays the results in three different 

graphs: the path to zero net energy graph 

located in the upper left corner of the screen, 

the end-use graph in the bottom left corner, 

and the option costs graph on the right side 

of the screen. 

7.1 Main Output Screen 

The upper left graph in Figure 9 shows 

optimal and near optimal points along the 

path to zero net energy. The x-axis displays 

the percent of energy saved compared to the 

base case, whereas the y-axis shows annual 

energy-related costs. Energy savings are 

calculated relative to a reference: either a 

user-defined base-case building or a climateFig. 9: Output screen for single optimization case (Phoenix)

11 
specific Building America Benchmark 

building automatically generated by the 

BEopt software. Energy-related costs 

include utility bills, the cost of efficiency 

measures included in the mortgage payment, 

and the present value of future replacement 

costs for options with lifetimes less than the 

analysis period, typically 30 years. Each 

marker represents a particular simulated 

building design. The curve connects the 

points with the lowest annualized energy 

cost for any given energy savings. For any 

selected point (or points), the BEopt 

software will display associated results in 

the End Use Breakdown and Options 

graphs. The user can quickly navigate to 

optimal points at specified energy savings. 

The user can also zoom in and select any 

point (or points) on the graph. The sequence 

through which the BEopt software 

determined the optimal points during the 

optimization can be followed one iteration at 

a time or replayed entirely by clicking the 

“step” and “play” buttons on the results 

toolbar. 

The lower left graph in Figure 9 shows enduse values (gas miscellaneous, gas hot 

water, gas heating, electric cooling, etc.) as 

colored stacked bars. The left-hand bar 

shows results for the base case; the righthand bar shows results for the selected point. 

The user can select whether the y-axis 

shows building energy consumption in terms 

of source energy (MBtu/year), site energy 

(MBtu/year), or cost of energy ($/year) and 

whether the current point results are relative 

to the Building America benchmark (or base 

case) or the previous point. If multiple 

points are selected in the upper left graph, 

the corresponding results are shown here. 

The right-hand graph in Figure 9 displays 

option data for a selected point or points. 

The range of possible options is shown in 

Fig. 10: Options sensitivity graph 

12 
light grey. For the currently selected point 

(50% source-energy savings in this 

example), options are shown in dark blue, 

and the options for the previous point (40% 

source-energy savings) are shown in 

magenta. Option names are shown to the 

right, and option costs are shown relative to 

the base case and totaled at the bottom of the 

column. If multiple points are selected in the 

upper left graph, the corresponding results 

are shown here. 

7.2 Option Sensitivity Graphs 

As another alternative to the upper left 

graph, option sensitivity graphs are available 

for each category (see Figure 10 for window 

type, for example). For each option, a curve 

shows the lower boundary of cash flow 

values for all building designs incorporating 

that option, based on all simulation results 

from the optimization. This type of graph 

shows the range of energy savings over 

which certain options are optimal and how 

far other options are from being competitive 

at a given level of energy saving. 

7.3 Detailed Output Displays 

For any simulation result, hourly results can 

be generated and various detailed outputs 

displayed (Figure 11). 

Fig. 10: Sample of BEopt detailed output displays.

Fig. 11: Sample of detailed output displays 

13 
7.4 Combined Graphs 

When two or more cases contain output, a 

tab appears to the right of all other case tabs. 

This tab displays an output screen (Figure 

12) with all cases, or a user-selected subset 

of cases, compared on the same three graphs 

previously described. 

The path-to-zero-net-energy curves are 

overlaid, and the end-use graph compares 

results for zero-net-energy designs for each 

case. The right-hand graph shows 

comparative options data for all cases. For 

the base case, option data are shown in a 

non-combined graph. For other cases, 

options are only shown if they are different 

than in the base case; indicators (red and 

green bars) show the direction and 

magnitude of the differences. To make the 

comparative options graph as concise as 

possible, the BEopt software automatically 

selects the case with the fewest differences 

with other cases to be the base case and then 

orders the other cases beginning with those 

with the fewest differences 

 8. Summary 

The BEopt computer program uses 

simulation and optimization to find optimal 

and near-optimal building designs along the 

path to zero net energy. To facilitate 

effective use of optimization, the BEopt user 

interface includes the following features: 

• A main input screen that allows the user 

to select, from many predefined options, 

FiFig. 11 BEopt output screen r multiple cases g. 12: Out : put screen for multi fo ple cases . 

14 
those to be used in the optimization 

• An output screen that allows the user to 

display detailed results for many optimal 

and near-optimal building designs 

• An options library spreadsheet that 

allows a user to review and modify 

detailed information on all available 

options 

• Energy savings that are calculated 

relative to a reference: either a userdefined base-case building or a climatespecific Building America Benchmark 

building 

• Multiple cases that allow analysis of 

different climates, economic parameters, 

PV system characteristics, or the options 

selected for optimization 

• Combined graphs that allow comparison 

of results from multiple cases. 

9. Acknowledgments 

This work was supported by the U.S. 

Department of Energy, Office of Building 

Technologies. The support of Ed Pollock 

George James, and Lew Pratsch Building 

America, and is gratefully acknowledged. 

We also thank Ron Judkoff of NREL’s 

Center for Buildings and Thermal Systems, 

along with Ed Hancock and Paul Reeves for 

their valuable discussions and contributions 

on the topic of this paper. 

This work has been authored by an 

employee of the Midwest Research Institute 

under Contract No. DE-AC36-99GO10337 

with the U.S. Department of Energy. The 

United States Government retains and the 

publisher, by accepting the article for 

publication, acknowledges that the United 

States Government retains a non-exclusive, 

paid-up, irrevocable, worldwide license to 

publish or reproduce the published form of 

this work, or allow others to do so, for 

United States Government purposes. 

10. References 

[1] Davis Energy Group. 1993. “ACT2 

Stanford Ranch Site, Final Design 

Report.” Davis, CA: Davis Energy 

Group. 

[2] EnergyGauge Pro. 2006. Cocoa, FL: 

Florida Solar Energy Center 

(http://energygauge.com/FlaRes/feature

s/pro.htm). 

[3] Wetter, M. 2001. “GenOpt®, Generic 

Optimization Program.” Seventh 

International IBPSA Conference, Rio 

de Janeiro, Brazil 

(www.ibpsa.org/bs_01.htm). 

[4] Hendron, R. 2005. Building America 

Research Benchmark Definition. 

NREL/TP-550-37529. Golden, CO: 

National Renewable Energy 

Laboratory. 

[5] York, D. and C. Cappiello. eds. 1981. 

DOE-2 Reference Manual (Version 

2.1A). Berkeley, CA: Lawrence 

Berkeley National Laboratory. 

[6] Klein, S., et al. 1996. TRNSYS: A 

Transient System Simulation Program – 

Reference Manual. Madison, WI: Solar 

Energy Laboratory, University of 

Wisconsin. 

[7] Marion, W. and D. Urban. 1995. User’s 

Manual for TMY2s – Typical 

Meteorological Years. Derived from the 

1961-1990 National Solar Radiation 

Data Base. Golden, CO: National 

Renewable Energy Laboratory. 

[8] King, D. L., J. A. Kratochvil, and W. E. 

Boyson. 1998. Field Experience with a 

New Performance Characterization 

Procedure for Photovoltaic Arrays. 

Proceedings of the 2nd World 

Conference and Exhibition on PV Solar 

Energy Conversion. 

15 
[9] EnergyPlus. 

www.eere.energy.gov/buildings/energ

yplus/. 

[10] Christensen, C., G. Barker, and S. 

Horowitz. 2004. A Sequential Search 

Technique for Identifying Optimal 

Building Designs on the Path to Zero 

Net Energy. Proceedings of the Solar 

2004, Portland, OR: American Solar 

Energy Society. 

[11] R. S. Means. 2005. R.S.Means 

Residential Cost Data, 24th Annual 

Edition. Kingston, MA: RSMeans 

Construction Publishers & 

Consultants. 

[12] Database for Energy Efficient 

Resources (www.energy.ca.gov/deer/). 

[13] eQUEST, QUick Energy Simulation 

Tool (www.doe2.com/equest). 

[14] SketchUp (www.sketchup.google.com). 

16 
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5a. CONTRACT NUMBER

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4. TITLE AND SUBTITLE

BEopt Software for Building Energy Optimization: Features and 

Capabilities 

5c. PROGRAM ELEMENT NUMBER

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NREL/TP-550-39929 

5e. TASK NUMBER

BET6.8004 

6. AUTHOR(S)

Craig Christensen, Ren Anderson, Scott Horowitz, Adam Courtney, 

Justin Spencer 

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

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14. ABSTRACT (Maximum 200 Words)

BEopt is a computer program designed to find optimal building designs along the path to ZNE. A user selects from 

predefined options in various categories to specify options to be considered in the optimization. Energy savings are 

calculated relative to a reference. The reference can be either a user-defined base-case building or a climatespecific Building America Benchmark building automatically generated by BEopt. The user can also review and 

modify detailed information on all available options in a linked options library spreadsheet. BEopt calls the DOE2 and 

TRNSYS simulation engines and uses a sequential search technique to automate the process of identifying optimal 

building designs along the path to ZNE. BEopt finds these optimal and near-optimal designs based on discrete 

building options reflecting realistic construction options. BEopt handles special situations with positive or negative 

interactions between options in different categories. The BEopt software includes a results browser that allows the 

user to navigate among different design points and retrieve detailed results regarding energy end-use and option 

costs in different categories. Multiple cases, based on a selected parameter such as climate, can be included in a 

BEopt project file for comparative purposes. 

15. SUBJECT TERMS

Building America; U.S. Department of Energy; BEopt; building energy simulation software; building energy 

optimization; building energy use modeling 

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Bringing you a prosperous future where energy is clean, abundant, reliable, and affordable

A Strong Energy Portfolio 

for a Strong America

Energy efficiency and clean, renewable 

energy will mean a stronger economy, 

a cleaner environment, and greater 

energy independence for America. 

Working with a wide array of state, 

community, industry, and university 

partners, the U.S. Department of 

Energy’s Office of Energy Efficiency 

and Renewable Energy invests in a 

diverse portfolio of energy technologies.

Research and Development 

of Buildings

Our nation’s buildings consume more 

energy than any other sector of the 

U.S. economy, including transportation 

and industry. Fortunately, the opportunities to reduce building energy use—

and the associated environmental 

impacts—are significant.

DOE’s Building Technologies Program 

works to improve the energy efficiency 

of our nation’s buildings through innovative new technologies and better 

building practices. The program

focuses on two key areas:

• Emerging Technologies 

Research and development of the 

next generation of energy-efficient 

components, materials, and 

equipment

• Technology Integration

Integration of new technologies 

with innovative building methods 

to optimize building performance 

and savings

For more information contact:

EERE Information Center

1-877-EERE-INF (1-877-337-3463)

www.eere.energy.gov

An electronic copy of this publication is 

available on the Building America Web 

site at www.buildingamerica.gov

U.S. Department of Energy

Energy Efficiency

and Renewable Energy

Building America Program

George S. James • New Construction • 202-586-9472 • fax: 202-586-8134 • e-mail: George.James@ee.doe.gov

Terry Logee • Existing Homes • 202-586-1689 • fax: 202-586-4617 • e-mail: terry.logee@ee.doe.gov

Lew Pratsch • Integrated Onsite Power • 202-586-1512 • fax: 202-586-8185 • e-mail: Lew.Pratsch@hq.doe.gov

Building America Program • Office of Building Technologies, EE-2J • U.S. Department of Energy • 1000 Independence Avenue, S.W. • 

Washington, D.C. 20585-0121 • www.buildingamerica.gov

Building Industry Research Alliance (BIRA)

Robert Hammon • ConSol • 7407 Tam O’Shanter Drive #200 • Stockton, CA 95210-3370 • 209-473-5000 • fax: 209-474-0817 • 

e-mail: Rob@consol.ws • www.bira.ws

Building Science Consortium (BSC)

Betsy Pettit • Building Science Consortium (BSC) • 70 Main Street • Westford, MA 01886 • 978-589-5100 • fax: 978-589-5103 • 

e-mail: Betsy@buildingscience.com • www.buildingscience.com

Consortium for Advanced Residential Buildings (CARB)

Steven Winter • Steven Winter Associates, Inc. • 50 Washington Street • Norwalk, CT 06854 • 203-857-0200 • fax: 203-852-0741 • 

e-mail: swinter@swinter.com • www.carb-swa.com

Davis Energy Group

David Springer • Davis Energy Group • 123 C Street • Davis, CA 95616 • 530-753-1100 • fax: 530-753-4125 •

e-mail: springer@davisenergy.com • deg@davisenergy.com • www.davisenergy.com/index.html

IBACOS Consortium

Brad Oberg • IBACOS Consortium • 2214 Liberty Avenue • Pittsburgh, PA 15222 • 412-765-3664 • fax: 412-765-3738 •

e-mail: boberg@ibacos.com • www.ibacos.com

Industrialized Housing Partnership (IHP)

Subrato Chandra • Florida Solar Energy Center • 1679 Clearlake Road • Cocoa, FL 32922 • 321-638-1412 • fax: 321-638-1439 • 

e-mail: subrato@fsec.ucf.edu • www.baihp.org

National Association of Home Builders (NAHB) Research Center

Tom Kenney • National Association of Home Builders (NAHB) Research Center • 400 Prince George’s Boulevard • 

Upper Marlboro, MD 20774 • 301-430-6246 • fax: 301-430-6180 • toll-free: 800-638-8556 • www.nahbrc.org/ 

National Renewable Energy Laboratory

Ren Anderson • 1617 Cole Boulevard, MS-2722 • Golden, CO 80401 • 303-384-7433 • fax: 303-384-7540 • 

e-mail: ren_anderson@nrel.gov • www.nrel.gov

Tim Merrigan • 1617 Cole Boulevard, MS-2722 • Golden, CO 80401 • 303-384-7349 • fax: 303-384-7540 • 

e-mail: tim_merrigan@nrel.gov • www.nrel.gov

Oak Ridge National Laboratory

Pat M. Love • P.O. Box 2008 • One Bethel Valley Road • Oak Ridge, TN 37831 • 865-574-4346 • fax: 865-574-9331 • 

e-mail: lovepm@ornl.gov • www.ornl.gov

Produced for the U.S. Department of Energy (DOE) by the National Renewable Energy Laboratory, a DOE national laboratory.

August 2006 • NREL/TP-550-39929

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