A national laboratory of the U.S. Department of Energy 

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Innovation for Our Energy Future 

BEopt: Software for Conference Paper

NREL/CP-550-37733 Identifying Optimal Building April 2005 

Designs on the Path to Zero 

Net Energy 

Preprint 

Craig Christensen 

National Renewable Energy Laboratory 

Scott Horowitz, Todd Givler, Adam Courtney 

University of Colorado 

Greg Barker 

Mountain Energy Partnership 

To be presented at ISES 2005 Solar World Congress 

Orlando, Florida 

August 6–12, 2005 

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BEOPT: SOFTWARE FOR IDENTIFYING OPTIMAL BUILDING DESIGNS 

ON THE PATH TO ZERO NET ENERGY 

Craig Christensen Scott Horowitz, Todd Givler, Greg Barker 

National Renewable Energy Laboratory Adam Courtney Mountain Energy Partnership 

1617 Cole Blvd. University of Colorado 13900 North 87th St. 

Golden, CO 80401 428 UCB, ECOT 441 Longmont CO 80503 

craig_christensen@nrel.gov Boulder, CO 80309 gbarker123@aol.com 

ABSTRACT 

A zero net energy (ZNE) building produces as much energy 

on-site as it uses on an annual basis, using a grid-tied, netmetered photovoltaic (PV) system and active solar. The 

optimal path to ZNE extends from a base case to the ZNE 

building through a series of energy-saving building designs 

with minimal energy-related owning and operating costs. 

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 climate-specific 

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 optimal and near-optimal designs along 

the path 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. 

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, net-metered PV and active solar to produce as 

much energy on-site as it uses annually. 

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. For electricity purchased from a 

utility, site energy can be converted to source energy to 

account for power plant generation efficiency and electrical 

transmission and distribution losses. The source-to-site 

energy ratio for electricity typically has a value of about 3, 

depending on the mix of electrical generation types (coalfired, natural gas combined cycle, nuclear, hydropower, 

etc.) 

2. THE PATH TO ZERO NET ENERGY 

Energy and cost results can be plotted in terms of annual 

costs, the sum of utility bills and mortgage payments for 

energy options, versus percent energy savings, as shown in 

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 

1
utility bills

Mortgage + Utilities ($/year)

2,500

2,000

1,500

1,000

500

0

0%100%

Energy Savings (%)

mortgage 

cash flow 

1 

2 

3 

4 

mortgage

utility bills 

cash flow

1

2

3

4

Fig. 1: Conceptual plot of the path to 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 are shown in 

Figure 1 and 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. 

3. BEOPT SOFTWARE 

BEopt is a computer program designed to find optimal 

building designs along the path to ZNE. In addition to an 

optimization search, BEopt 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 (1) 

in a linked options library spreadsheet. 

BEopt calls the DOE2 (2) and TRNSYS (3) simulation 

engines and automates the process of identifying optimal 

building designs along the path to ZNE using a sequential 

search technique (4). At each step along the path, BEopt 

runs individual simulations for all user-selected options and 

searches for the most cost-effective combination of options. 

The BEopt 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, reflecting 

realistic construction options. Third, multiple near-optimal 

designs are identified at each particular energy savings 

level, providing design alternatives. 

BEopt 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 re-evaluating previously rejected 

options (to properly identify the potential of large-savings 

options and options involving trade-offs between 

categories). Positive interactions between categories are 

accommodated by allowing the user to define combined 

options in BEopt (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, PV system 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. 

2
 Fig. 2: BEopt main input screen. 

4. INPUT SCREEN (OPTION SELECTION) 

Upon opening a project file, BEopt scans the options library 

spreadsheet and loads categories and options associated with 

the project (Figure 2). 

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 magnifying glass button located at 

the upper-right corner of the options box. This will open the 

options library (described later). 

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 BEopt to consider during optimization. 

In other situations, subsets of options may be mutually 

exclusive. In the Foundation category, for example, options 

exist for slab 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 or not subsets of 

options should be treated as mutually exclusive or not. For 

example, should the optimization include all space heating 

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). 

3
Fig. 3: BEopt output screen. 

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 BEopt. However, if the user has the 

technical experience to recognize this situation, an Include 

Combinations category (Figure 2, “Glass+Mass”) can be 

created 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, BEopt can run an optimization of 

the possible combinations of options. For selected options 

similar to those in Figure 2, optimization would involve 

approximately 1000 simulations and approximately 1.5 

hours of computer run-time. 

5. OUTPUT SCREEN (RESULTS BROWSER) 

Once an optimization process has been run, the output 

screen can be displayed. Figure 3 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. 

4
Fig. 4: BEopt options library spreadsheet. 

5.1 Path to Zero Net Energy Graph 

This graph details the 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 while the y-axis 

shows annual energy-related costs. Energy savings are 

calculated relative to a reference: either a user-defined basecase building or a climate-specific Building America 

Benchmark building automatically generated by BEopt. 

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 colored marker represents a particular simulated 

building design. Points shown in the same color and symbol 

shape are combinations that were simulated during a 

particular optimization iteration. (Therefore, the number of 

colors matches the number of iterations performed by 

BEopt.) The curve connects the points with the lowest 

annualized energy cost for any given energy savings. 

For any selected point, BEopt will display associated results 

in the End Use Breakdown and Options Costs graphs. The 

user can quickly navigate to optimal points at specified 

energy savings. The user can also zoom in and select any 

(non-optimal) point on the graph. The sequence through 

which BEopt determined the optimal points during the 

optimization can be followed one iteration at a time or 

5
replayed entirely by clicking the “step” and “play” buttons 

on the results toolbar. 

5.2 End Use Graph 

End use values (gas misc., gas hot water, gas heating, 

electric cooling, etc.) are shown 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). 

5.3 Options Cost Graph 

The options cost graph displays data by category with bar 

outlines indicating the maximum possible option cost within 

the set of selected options. For a selected point, the shaded 

bars and values indicate option costs. Option names are 

shown to the right. Option costs shown are relative to the 

base case and totaled at the bottom of the column. 

When two or more cases contain output, a Combined 

Graphs tab appears to the right of all other case tabs. This 

tab displays an output screen (there is no associated input 

screen) with all cases compared on the same three graphs 

previously described. The path-to-zero-net-energy curves 

are overlaid, while the option cost graph and the end use 

graph compare results for zero net energy designs for each 

case. 

6. OPTIONS LIBRARY 

The options library spreadsheet (Figure 4) contains detailed 

information for all the BEopt categories and options. While 

some users may only use the BEopt input screens to specify 

optimization inputs, 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). 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. 

7. CONCLUSIONS 

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: 

 The main input screen that allows the user to select 

from many predefined options, those to be used in the 

optimization, 

 The output screen that allows the user to display 

etailed results for many optimal and near-optimal

building designs, 

 The options library spreadsheet that allows a user to 

review and modify detailed information on all 

available options, 

 Energy savings calculated relative to a reference: 

either a user-defined 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, and 

 Combined graphs that allow comparison of results 

from multiple cases. 

8. ACKNOWLEDGMENTS 

This work was supported by the U.S. Department of Energy, 

Office of Building Technologies. The support of Ed 

Pollock and George James, Building America, and Lew 

Pratsch, Zero Energy Buildings, is gratefully acknowledged. 

We also thank Ren Anderson, and 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. 

9. REFERENCES 

(1) Hendron, R., Building America Research Benchmark 

Definition, NREL/TP-550-37529, National Renewable 

Energy Laboratory, February 2005 

(2) York, D. and Cappiello, C., eds., 1981, DOE-2 

Reference Manual (Version 2.1A), Lawrence Berkeley 

National Laboratory, Berkeley, CA 

(3) Klein, S., et al, 1996, TRNSYS: A Transient System 

Simulation Program – Reference Manual, Solar Energy 

Laboratory, University of Wisconsin, Madison, WI 

(4) Christensen, C., Barker, G., Horowitz, S., 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, 2004 

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14. ABSTRACT (Maximum 200 Words) 

A zero net energy (ZNE) building produces as much energy on-site 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 energy-saving building designs with minimal energy-related owning and operating 

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

from among 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 BEopt. The user can also review 

and modify detailed information on all available options and the Building America Benchmark in a linked options 

library spreadsheet. 

15. SUBJECT TERMS 

zero net energy building; net-metered PV system; active solar; ZNE; energy-related house costs; energy-efficient 

housing; Beopt 

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