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

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Searching for the Optimal Mix of 

Solar and Efficiency in Zero Net 

Energy Buildings 

Preprint 

S. Horowitz, C. Christensen, and R. Anderson 

National Renewable Energy Laboratory 

Presented at Solar 2008 

San Diego, California 

May 3–8, 2008 

Conference Paper 

NREL/CP-550-42956 

August 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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SEARCHING FOR THE OPTIMAL MIX OF SOLAR AND EFFICIENCY 

IN ZERO NET ENERGY BUILDINGS 

Scott Horowitz, Craig Christensen, Ren Anderson 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

scott_horowitz@nrel.gov 

craig_christensen@nrel.gov 

ren_anderson@nrel.gov 

ABSTRACT

Zero net energy (ZNE) buildings employ efficiency to 

reduce energy consumption and solar technologies to 

produce as much energy on site as is consumed on an 

annual basis. Such buildings leverage utility grids and 

net-metering agreements to reduce solar system costs and 

maintenance requirements relative to off-grid 

photovoltaic (PV)-powered buildings with batteries. 

The BEopt software was developed to efficiently identify 

cost-optimal building designs using detailed hour-by-hour 

energy simulation programs to evaluate the user-selected 

options. A search technique identifies optimal and nearoptimal building designs (based on energy-related costs) 

at various levels of energy savings along the path from a 

reference building to a ZNE design. 

In this paper, we describe results based on use of the 

BEopt software to develop cost-optimal paths to ZNE for 

various climates. Comparing the different cases shows 

optimal building design characteristics, percent energy 

savings and cash flows at key points along the path, 

including the point at which investments shift from 

building improvements to purchasing PV, and PV array 

sizes required to achieve ZNE. 

From optimizations using the BEopt software for a 2,000-

ft2

 house in 4 climates, we conclude that, relative to a 

code-compliant (IECC 2006) reference house, the 

following are achievable: 1) minimum cost point: 22 to 

38% source energy savings and 15 to 24% annual cash 

flow savings; 2) PV start point: 40 to 49% source energy 

savings at -10 to 12% annual cash flow savings; 3) breakeven point: 43 to 53% source energy savings at 0% annual 

cash flow savings; and 4) ZNE point: 100% source energy 

savings with 4.5 to 8.1 kWDC PV arrays and 76 to 169% 

increase in cash flow. 

1. BACKGROUND 

1.1. Types of Zero Energy Buildings

Off-grid photovoltaic (PV)-powered buildings are zero 

energy with regard to the electricity grid, but often use 

propane for space and water heating and to run a backup 

generator. However, the more recent concept of zero net 

energy (ZNE) buildings promises more widespread 

applicability in the U.S. housing sector. ZNE buildings 

use grid-tied, net-metered PV and active solar to produce 

as much energy as is used on an annual source-energy 

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. 

For electricity purchased from a utility, source energy 

accounts for power plant generation efficiency and 

transmission and distribution losses, and source-to-site 

ratios are typically about 3, depending on the mix of 

electricity generation types (coal-fired, natural-gas 

combined-cycle, nuclear, hydropower, etc.) For purchased 

natural gas and propane, source-to-site energy ratios 

include production energy as well as transmission and 

distribution losses, and are slightly greater than 1. 

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, as shown in Figure 1. The path to ZNE extends 

from a reference building (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 can be 

described as follows. From the reference building at point 

1, energy use is reduced by employing building efficiency 

options (improvements in wall R-value, furnace annual 

fuel utilization efficiency [AFUE], air conditioner 

seasonal energy efficiency ratio [SEER], etc.). A 

minimum annual cost optimum occurs at point 2 

(assuming the minimum does not occur at the reference 

building). Additional building efficiency options and solar 

water heating are employed until the marginal cost of 

saved energy for these options equals the cost of 

producing PV energy at point 3. The annual cost equals 

the annual cost of the reference building at point 4. From 

that point on, energy savings are solely a result of adding 

PV capacity until ZNE is achieved at point 5. 

3. BUILDING ENERGY OPTIMIZATION

Building energy simulations are often used for trial-anderror 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 & 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, 

10 or more categories. 

Several computer programs have been developed to 

automate building energy optimization. For example, 

EnergyGauge-Pro 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 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 to other target 

levels of energy savings between the reference building 

and ZNE and is the basis for establishing the optimal path 

to ZNE. 

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 

from discrete options in various categories. For example, 

options in the wall construction category may include 2×4 

R11, 2×4 R13, 2×6 R19, 2×6 R19 with 1-in. foam, and 

2×6 R19 with 2-in. foam. 

If discrete option characteristics for a particular category 

fall along a smooth curve, a continuous function could be 

0

500

1,000

1,500

0% 100%

Energy Savings (%)

Mortgage + Utilities

2,000

2,500

 ($/year)

mortgage

utility bills

cash flow

1

2

3

4

0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Mortgage + Utilities ($/year)

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

mortgage

utility bills

cash flow 54

1 4

2

3

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

2 
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. 

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, 

energy use predictions, and other parameters, nearoptimal points may be as good as optimal points. For 

various reasons unrelated to energy or cost, 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; others 

can be added with perturbation techniques. 

4. BEOPT SOFTWARE

The BEopt software uses a sequential search technique to 

automate the process of identifying optimal building 

designs along the path to ZNE (4). The selection of this 

technique was influenced by several factors. First, the 

method identifies intermediate optimal points all along 

the path of interest (i.e., minimum-cost building designs 

at different target energy savings levels), not just the 

global optimum or the ZNE optimum. Second, the method 

allows discrete rather than continuous building options to 

be evaluated, reflecting realistic construction options. 

Third, near-optimal alternative designs are identified 

along the path, allowing for substitution of essentially 

equivalent solutions based on builder or contractor 

preferences. 

The sequential search approach involves searching all 

categories (wall type, ceiling type, window glass type, 

HVAC type, etc.) for the most cost-effective combination 

at each sequential point along the path to ZNE. Starting 

with the reference building, simulations are performed to 

evaluate all available options for improvement (one at a 

time) in the building envelope, equipment, appliances, 

lighting, and solar water heating. Based on the results, the 

most cost-effective combination is selected as an optimal 

point on the path and put into a new building description. 

The process is repeated. At each step, the marginal cost of 

saved energy is calculated and compared with the cost of 

PV energy. From the point where further improvement in 

the building envelope or equipment has a higher marginal 

cost, the building design is held constant, and PV capacity 

is increased to reach ZNE. 

TRNSYS

SDHW

PV

The BEopt software calls the DOE2 (5) and TRNSYS (6) 

simulation engines. TMY 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 and 

the database of performance characteristics published on 

its web site (8). Perfect maximum power-point tracking is 

assumed. The inverter efficiency is assumed to follow the 

shape of a typical inverter, with a capacity of 1.2 times 

the rated PV array output at standard rating conditions. 

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 available for optimization. 

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 

Fig. 2: Optimization with multiple simulation engines

DOE2

EPW

Heating,

Cooling

Lighting,

Appliances

Optimal

Building

Designs

BEopt TMY

3 
the optimization; 2) an output screen that allows the user 

to display detailed results for many optimal and nearoptimal building designs; and 3) an options library 

spreadsheet that allows a user to review and modify 

detailed information on all available options. 

5. ASSUMPTIONS

5.1. Building Characteristics 

A simple two-story 2,000-ft2

 home with an attached twocar garage was used for this study (Figure 3). The home 

has a slab or basement foundation depending on climate. 

Window area was assumed to be 18% of floor area. The 

study was limited to one orientation (back facing south), 

allowing full exposure of the windows on the back of the 

home to winter sun. Adjacent homes 10 ft to the east and 

west provide shading of sidewalls of the house analyzed. 

The energy options considered in the study include spaceconditioning systems, envelope constructions, hot water 

systems (including tankless and solar hot water), lighting 

systems, major appliances, and grid-connected residential 

PV. No specific options that address miscellaneous 

electricity loads other than major appliances were 

included in the study. 

5.2. Economic Assumptions.

The homeowner costs calculated in the study assume a 

30-year mortgage at a 7% interest rate, 3% general 

inflation rate, a 5% nominal discount rate, and a 30-year 

analysis period. The net present value of replacements for 

options with lifetimes shorter than 30 years were included 

in option costs. 

5.3. Occupancy/Operational Assumptions

The occupancy and operational assumptions used in the 

study are defined in the Building America Research 

Benchmark (10) and include time-of-day profiles for 

occupancy, appliance and plug loads, lighting, domestic 

hot water use, ventilation, and thermostat settings. 

5.4. Reference Building Characteristics

Incremental energy savings and incremental home costs 

are calculated relative to a reference building of the same 

size, geometry, and occupancy/operation as the design 

building, which meets IECC 2006 (10) requirements for 

the climates studied. 

5.5. Cost Assumptions

Each option has an assumed first cost and lifetime. Costs 

used in the analysis represent retail costs and include 

estimated costs for hardware, installation labor, overhead, 

and profit. Construction costs (wall insulation, ceiling 

insulation, foundation insulation, etc.) are typically based 

on national average cost data (11). Window and HVAC 

costs are based on quotes from manufacturers’ 

distributors. Appliance costs are based on manufacturers’ 

suggested retail prices. Some energy efficiency option 

costs are based on a California database (12). 

Building construction options (wall insulation, ceiling 

insulation, foundation insulation, windows, etc.) are 

assumed to have 30-year lifetimes. Equipment and 

appliance options typically have 10- or 15-year lifetimes. 

Lifetimes for lighting options (incandescent and compact 

fluorescent lamps [CFLs]) are modeled based on 

cumulative hours of use. The home is assumed to have a 

gas water heater (or solar hot water heater with gas 

backup), a gas furnace, a gas clothes dryer, and a gas 

stove. 

Utility costs based on state average prices (13) for 

electricity and natural gas are 0.082, 0.134, 0.085, and 

0.079 $/kWh and 1.68, 1.22, 1.33, and 1.16 $/Therm for 

Phoenix, San Diego, Atlanta, and Chicago, respectively 

The onsite power option used for this study was a 

residential PV system with an installed cost of $7.50 per 

peak WattDC, including present value of future operation 

and maintenance costs. 

Fig. 3. House geometry with neighboring houses used for shading analysis. 

4 
6. RESULTS

6.1. Climate-Specific Results

Analysis results for Phoenix, San Diego, Atlanta, and 

Chicago are shown in Figures 4 through 7. All the figures 

include three specific graphs. 

The upper left graph shows optimal and near optimal 

points along the path to ZNE. The x-axis displays the 

percent of source energy saved compared to the reference 

building. The y-axis shows annual energy-related costs 

including utility bills, the cost of efficiency measures 

included in the mortgage payment, and the present value 

of future replacement costs for options with lifetimes 

shorter than the analysis period. Each point represents an 

actual simulated building design. The curve connects the 

points with the lowest annualized energy cost for any 

given energy savings. The straight line sloping up to the 

right shows the addition of PV in 0.5-kW increments. 

The lower left graph shows end use values (gas 

miscellaneous, gas hot water, gas heating, electric 

cooling, etc.) as colored stacked bars. The left-hand bar 

shows results for the reference building; the right-hand 

bar shows results for the ZNE point. Building source 

energy use (MBtu/year) consumption is shown on y-axis. 

The PV energy production is shown by the horizontal 

black line, and the net energy consumption (energy use 

minus PV production) is shown by the number in 

parenthesis – zero for the ZNE design. 

The right-hand graph displays option data for the ZNE 

design. The range of possible options is shown in light 

gray, and the reference building option is shown in dark 

gray. The options for the ZNE design are shown in dark 

blue. Option names are shown to the right, and option 

costs are shown relative to the reference building. 

6.2. Climate Comparison Results

Figure 8 shows combined results for all four climates. In 

the upper left graph, the path-to-ZNE curves are overlaid, 

and the reference building source-energy use and PV 

array sizes are shown for each climate. In the lower left 

graph, the end use graph shows results for ZNE designs 

for each climate. 

The right-hand graph shows comparative options data for 

all cases. For the San Diego (user-selected as the base for 

comparison because it is the mildest climate), option data 

are displayed as in the right-hand graphs in Figures 4 

through 7. For other climates, options are only shown if 

they are different than in San Diego with indicators (red 

and green bars) showing the direction and magnitude of 

the differences. Several of the differences shown in 

Figure 8 are simply the result of the fact that the Chicago 

building has a basement while the buildings in the other 

climates have slab-on-grade floors. 

The following options were chosen for ZNE designs in all 

climates: 

• window area – shifted to the back (south) 

• appliances – high efficiency 

• fixed and plug-in lighting – CFLs 

• water heaters – tankless 

• ducts – inside conditioned space 

• PV size – sufficient for ZNE 

Compared to San Diego, the other climates show: 

• wall insulation – more in hotter/colder climates 

• ceiling insulation – more, except Phoenix 

• roof reflectance – more in hotter climates 

• radiant barrier – only in Phoenix 

• infiltration – tighter in hotter/colder climates 

• slab insulation – more in Atlanta 

• wall mass – more in colder climates 

• window type – solar heat gain coefficient 

depends on climate 

• dishwasher – less efficient in Phoenix 

• air conditioner – higher SEER in other climates 

• furnace – higher AFUE in colder climates 

• mechanical ventilation – Chicago has heat 

recovery ventilator 

• solar water heating – larger in colder climates 

Many of these results are consistent with expectations as a 

function of climate characteristics. A few unexpected 

results, however, warrant further comment: 1) the less 

efficient dishwasher in Phoenix is perhaps due to lower 

hot water loads attributable to higher temperatures of 

incoming cold water (mains water temperature is a 

function of annual average ambient temperature); and 2) 

higher SEER air conditioners in colder climates are likely 

due to heating benefits from two-speed operation and 

high-efficiency fans that come with the air handlers 

associated with high-SEER air conditioners. 

Table 1 shows results from Figures 4 through 7 for the 

four climates. The numbered columns (major headings) in 

Table 1 correspond to the design points in Figure 1. 

Column 1 (reference point) shows source energy values 

ranging from 185 MBtu/year in Phoenix to 203 

MBtu/year in Chicago, with a minimum for San Diego, 

which has neither the heating nor the cooling extremes of 

the other climates. Energy-related cash flows range from 

$1,450/year in San Diego to $2,100/year in Atlanta. 

Column 2 (minimum cost point) shows lower energy 

savings relative to the reference building in colder 

climates. This is likely due to more stringent U-value 

requirements in the IECC 2006 in cold climates. The cash 

flows savings (which are maximized at this point) range 

from 15% in Chicago to 24% in Phoenix.

5 
Fig. 4. ZNE results for Phoenix, Arizona.

Fig. 5. ZNE results for San Diego, California.

6 
Fig. 6. ZNE results for Atlanta, Georgia.

Fig. 7. ZNE results for Chicago, Illinois.

7 
Fig. 8. Combined ZNE results for Phoenix, San Diego, Atlanta, and Chicago. 

TABLE 1: SAVINGS IN SOURCE ENERGY AND CASH FLOW 

(1) 

Reference Point 

(2) 

Min. Cost Point 

(3) 

PV Start Point 

(4) 

Break-Even 

Point 

(5) 

ZNE Point 

Source 

Energy 

(MBtu/y) 

Cash 

Flow 

($/y) 

Source 

Energy1

(%) 

Cash 

Flow1

(%) 

Source 

Energy1

 (%) 

Cash 

Flow1

(%) 

Source 

Energy1

 (%) 

Cash 

Flow1

(%) 

Source 

Energy1

 (%) 

Cash 

Flow1

(%) 

Array 

Size 

(kWDC)

Phoenix 185 1700 38 24 46 12 50 0 100 -106 5.9 

San 

Diego 116 1450 32 17 40 12 46 0 100 -76 4.5 

Atlanta 175 2100 34 21 49 10 53 0 100 -95 6.2 

Chicago 203 1950 22 15 49 -10 43 0 100 -169 8.1 

1

 Positive values indicate percent reductions relative to the reference building; negative values indicate percent increases. 

8 
Column 3 (PV start point) shows energy savings of 40 to 

49% and cash flow savings of 10 to 12%, except for 

Chicago, where efficiency improvements continue beyond 

the break-even point and cash flow is 10% more than in the 

reference building. 

Column 4 (break-even point) shows source energy savings 

ranging from 43% in Chicago to 53% in Atlanta at cash 

flows equal to the reference building cash flows. 

Column 5 (ZNE point) shows source energy savings 

(100%), cash flows, and PV array sizes. The PV array size 

(kW) for each climate is a function of: 1) the local solar 

resource and 2) the PV energy needed to be supplied, i.e., 

the reference building source energy multiplied by (1 – 

percent savings at PV Start). Phoenix, of course, benefits 

from an excellent solar resource while San Diego has the 

smallest array size due to low overall building source energy 

consumption. 

7. SUMMARY

Based on optimizations using the BEopt software for a 

2,000-ft2

 house in 4 climates, we conclude that, relative to a 

code-compliant (IECC 2006) reference house, the following 

are achievable: 

• Minimum Cost Point: 22 to 38% source energy 

savings and 15 to 24% annual cash flow savings 

• PV Start Point: 40 to 49% source energy savings at 

-10 to 12% annual cash flow savings 

• Break-even Point: 43 to 53% source energy 

savings at 0% annual cash flow savings 

• ZNE Point: 100% source energy savings with 4.5 

to 8.1 kWDC PV arrays and 76 to 169% increase in 

cash flow 

As with any analysis study, the results of the analysis are 

subject to the assumptions used during the study. Data from 

ongoing residential system field studies will be used to 

validate and update the component cost and performance 

models used in the present study in collaboration with the 

project’s research teams. 

8. ACKNOWLEDGMENTS

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

Office of Building Technologies. The support of Ed 

Pollock, manager of the Building America Program, is 

gratefully acknowledged. We also thank Ron Judkoff of 

NREL’s Center for Buildings and Thermal Systems. 

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. 

9. REFERENCES

(1) Davis Energy Group. 1993. “ACT2 Stanford Ranch Site, 

Final Design Report.” Davis, CA: Davis Energy Group. 

(2) EnergyGauge Pro. 2006. Cocoa, FA: Florida Solar 

Energy Center 

(http://energygauge.com/FlaRes/features/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) 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. 

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

(9) Hendron, R. 2005. Building America Research 

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

National Renewable Energy Laboratory. 

(10) International Energy Conservation Code 2006, 

International Code Council, Whittier, CA, www.iccsage.org. 

 (11) R. S. Means. 2005 / 2008. R.S.Means Residential Cost 

Data, Kingston, MA: RSMeans Construction Publishers & 

Consultants. 

(12) Database for Energy Efficient Resources 

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

(13) U.S. Energy Information Administration 

http://www.eia.doe.gov 

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4. TITLE AND SUBTITLE

Searching for the Optimal Mix of Solar and Efficiency in Zero Net 

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5d. PROJECT NUMBER

NREL/CP-550-42956 

5e. TASK NUMBER

BET88001 

5f. WORK UNIT NUMBER

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

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION

REPORT NUMBER

NREL/CP-550-42956 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Zero net energy (ZNE) buildings employ efficiency to reduce energy consumption and solar technologies to produce 

as much energy on site as is consumed on an annual basis. Such buildings leverage utility grids and net-metering 

agreements to reduce solar system costs and maintenance requirements relative to off-grid photovoltaic-powered 

buildings with batteries. The BEopt software was developed to efficiently identify cost-optimal building designs using 

detailed hour-by-hour energy simulation programs to evaluate the user-selected options. A search technique 

identifies optimal and near-optimal building designs (based on energy-related costs) at various levels of energy 

savings along the path from a reference building to a ZNE design. 

15. SUBJECT TERMS

zero net energy; zne; net metering agreement; solar system; photovoltaic; pv 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

Prescribed by ANSI Std. Z39.18