National Renewable Energy Laboratory 

Innovation for Our Energy Future 

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

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Analysis of Residential System 

Strategies Targeting Least-Cost 

Solutions Leading to Net Zero 

Energy Homes 

Preprint 

R. Anderson and C. Christensen 

National Renewable Energy Laboratory 

S. Horowitz 

University of Colorado 

To be presented at the ASHRAE 2006 Annual Meeting 

Session: How Low Can You Go? Low-Energy Buildings through 

Integrated Design 

Quebec City, Canada 

June 24–28, 2006 

Conference Paper 

NREL/CP-550-38170 

April 2006 
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Analysis of Residential System 

Strategies Targeting Least-Cost 

Solutions Leading to Net Zero Energy 

Homes 

Ren Anderson and Craig Christensen 

National Renewable Energy Laboratory 

Scott Horowitz 

University of Colorado 

ABSTRACT 

The U. S. Department of Energy’s Building America residential systems research project uses an 

analysis-based system research approach to identify research priorities, identify technology gaps and 

opportunities, establish a consistent basis to track research progress, and identify system solutions that are 

most likely to succeed as the initial targets for residential system research projects. 

This report describes the analysis approach used by the program to determine the most cost-effective 

pathways to achieve whole-house energy-savings goals. This report also provides an overview of 

design/technology strategies leading to net zero energy buildings as the basis for analysis of future 

residential system performance. 

BACKGROUND 

System Research Objectives 

The objectives of the current research project are as follows: 

1. Develop integrated energy efficiency and onsite/renewable power solutions that can be successfully 

used on a production basis to reduce whole-house energy use in new homes by an average of 50% by 

2015 and 90% by 2025, 

2. Integrate key energy systems innovations from research in new homes into existing homes. 

For innovative building energy technologies to be viable candidates over conventional approaches, it 

must be demonstrated that they can cost-effectively increase overall product value and quality, while 

significantly reducing energy use and use of raw materials when used on a production basis. The project's 

team-based systems research approach, including use of systems engineering research techniques, provides 

opportunities for cost and performance trade-offs that improve whole-building performance and value, 

while minimizing increases in overall building cost. Systems research is conducted at multiple scales, 

including individual test houses, pre-production houses, and community-scale developments. Systems 

research includes analysis of system performance and cost tradeoffs as they relate to whole-building energy 

performance and cost optimization, including interactions between advanced envelope designs, mechanical 

and electrical systems, lighting systems, space conditioning systems, hot water systems, appliances, plug 

loads, energy control systems, renewable energy systems, and onsite power generation systems. 

A systems research approach creates process innovations that improve efficiency and flexibility of 

housing production. Systems research also improves control over component interactions, which further 

improves home efficiency and performance. In addition, a systems research approach increases value, 

reduces risks, reduces barriers, and accelerates adoption of new technologies by increasing integration 

1 
between the design and construction process, increasing system performance, increasing system cost 

effectiveness, and increasing system reliability and durability. Test-house-scale, subdivision-scale, and 

community-scale evaluation of advanced system concepts in partnership with builders, contractors, and 

state and local governments provide opportunities for early adopters and industry leaders to directly 

contribute to key results from the research program. 

 For development of advanced residential buildings, a systems approach (Figure 1) is defined to be any 

approach that utilizes comprehensive examination and analysis of overall design, delivery, business 

practices, and construction processes (including financing) and that performs cost and performance 

tradeoffs between individual building components and construction steps to produce a net improvement in 

overall building value and performance. A systems approach includes the use of systems engineering and 

Figure 1. Overview of Building America systems engineering process 

operations research techniques. It also requires integrated participation and team building among all parties 

interested in the building process, including developers, architects, designers, engineers, builders, 

equipment manufacturers, material suppliers, community planners, mortgage lenders, state and local 

governments, utilities, and others. Performance results from the evaluation of these systems are presented 

to a broad residential building science audience via development of technical papers, presentations at major 

building industry conferences, development of building system performance packages, and development of 

"train the trainer" curricula based on the key results of the research program.1

Source and Site Energy Accounting 

Energy savings can be defined in terms of site energy (used at the building site) or source energy 

(sometimes called primary energy). For electricity purchased from a utility, site energy can be converted to 

source energy as a way of accounting for power plant generation efficiency and electrical transmission and 

 

1

 Building America research results and education materials may be found on 

http://www.buildingamerica.gov/

2 
distribution losses. All energy savings in this paper are based on source energy savings. The source-to-site 

energy ratio for electricity typically has a value of about 3, depending on the mix of electrical generation 

types (coal-fired, natural gas combined cycle, nuclear, hydropower, etc.) From the view of all stakeholders 

in the building process, site and source energy are both important. Source energy has been chosen as the 

basis for tracking progress toward the energy saving targets for the program and will also be used as the 

basis of the cost/performance tradeoffs analyzed in this report. Site energy savings are also calculated as 

part of ongoing research projects and included in project evaluations because of their importance in 

determining specific utility bill savings. 

ANALYZING THE LEAST-COST PATH TO HOMES THAT PRODUCE AS MUCH ENERGY AS 

THEY USE ON AN ANNUAL BASIS 

The research path to future residential energy savings extends from a base case (e.g., a current-practice 

building, a code-compliant building, or some other reference building) to a Zero Net Energy (ZNE) 

building with 100% source energy savings2

. To ensure a well-defined reference for evaluation of energy 

savings and progress toward multi-year goals, a detailed benchmark building definition has been developed 

for use by all participants in the project (Hendron 2005). A standard reporting format for research results 

has also been developed to facilitate comparisons of performance between different research projects 

(Hendron et al. 2004). 

To evaluate the cost required to reach a specific energy target, 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 2. The optimal least-cost path can then be determined by connecting the 

points for building designs that achieve various levels of energy savings at minimal cost (i.e., that establish 

the lower bound of results from all possible building designs). Alternatively, net present value or other 

economic figures of merit could be chosen. Inclusion of even a modest number of possible options for 

major system choices can lead to a very large number of possible building designs. One of the key 

challenges in developing a practical analysis method is to develop an approach that quickly focuses on the 

combinations that are nearest to the least-cost limit. 

Points of particular interest on the least cost path to net zero energy homes are shown in Figure 2 and 

can be described as follows: from the Research Benchmark at point 1, energy use is reduced by employing 

building efficiency options (e.g., improvements in space conditioning systems, hot water systems, lighting 

systems, thermal distribution systems, etc.) A minimum annual cost optimum occurs at point 2. Additional 

building efficiency options are employed until the marginal cost of saving energy for these options equals 

the cost of producing onsite power at point 3. In this study, residential PV systems are used as the system 

option for onsite power. As research on distributed energy systems continues, it is anticipated that other 

onsite power technologies will also become available for residential-scale projects. From point 3 on, the 

building design does not change and energy savings are solely a result of adding additional onsite power 

capacity, until ZNE is achieved at point 4. 

 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 a successful California 

project, energy efficiency measures were evaluated using DOE2 simulations in a sequential analysis 

method that explicitly accounted for interactions (Davis Energy Group, 1993). 

Several computer programs to automate building energy optimization have been recently developed. 

For example, EnergyGauge-Pro uses successive, incremental optimization (similar to the approach used in 

the California project referenced above) with calculations based on the “energy code multiplier method” for 

Florida (Florida Solar Energy Center 2005). GenOpt is a generic optimization program for use with 

various building energy simulation programs and user-selectable optimization methods (Wetter 2004). 

 

2

 Energy savings include credit for energy from an onsite power system that is delivered to the grid minus 

energy used by the onsite power system. 

3 
0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Total Annual Costs ($/year)

mortgage

utility bills

cash flow

1

2

3

4

0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Total Annual Costs ($/year)

mortgage

utility bills

cash flow

1

2

3

4

0

500

1,000

1,500

2,000

2,500

0% 100%

Energy Savings (%)

Total Annual Costs ($/year)

mortgage

utility bills

cash flow

1

2

3

4

Figure 2. Conceptual plot of the least-cost path to a ZNE home 

CONSTRAINED VERSUS GLOBAL OPTIMIZATION 

From a purely economic point of view, building energy optimization involves finding the global 

optimum (the minimum annual cost point 2 in Figure 2) that balances investments in efficiency versus 

utility bill savings. Given a particular energy savings target, economic optimization can be used to 

determine the optimal design (lowest cost) to achieve the energy savings 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. 

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 can be used in an optimization methodology along with other discrete and continuous categories. 

After optimization, the discrete options closest to the optimal values can 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. 

Near-Optimal Solutions 

It is advantageous for the optimization methodology to present multiple solutions (optimal and nearoptimal). Near-optimal solutions achieve a particular level of energy savings with total costs close to the 

optimal solution total cost. Given uncertainty in cost assumptions and energy use predictions, near-optimal 

points may be as good as optimal points. For various non-energy/cost reasons, the alternative construction 

4 
options in near-optimal solutions may be of interest to building designers to facilitate substitutions that 

meet target market needs without compromising overall system energy performance. 

Evaluation of Other Market Drivers in Addition to Energy Cost 

The least-cost options identified by the analysis method shown in Figure 2 represent a zero constraint 

starting point for system studies by project research teams in partnership with the residential construction 

industry. The proposed analysis method does not currently include models to evaluate the impacts of nonenergy market drivers such as durability, reliability, ease of installation, availability of local supply, 

service, and support centers, or warranty and call-back costs. The initial analysis results presented in this 

paper are therefore limited to determining the minimum requirement, based on marginal cost and energy 

performance, for a given design/technology combination to be considered as viable system solution on the 

least-cost curve. On average, it currently takes about 3 years to evaluate the expected performance benefits 

of new system concepts, integrate systems into test homes, and evaluate final cost and performance benefits 

when implemented on a production basis. 

IMPLEMENTATION OF A NEW SEQUENTIAL SEARCH TECHNIQUE 

The BEopt Analysis Method 

In previous papers (Christensen et al. 2003a, 2003b) , we described methods to determine the least-cost 

path to ZNE homes based on the marginal costs of energy efficiency and renewable energy options. We 

developed methods to determine the path to ZNE by curve fitting a few key points found by optimization 

using the costs of utility energy and PV energy. 

The BEopt analysis method uses an efficient sequential search technique to find optimal and near 

optimal combinations of discrete energy efficiency options (Christensen et al. 2004). The development of 

this analysis method 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, an additional 

benefit of the search strategy is the identification of near-optimal alternative designs along the path, 

allowing for substitution of nearly equivalent solutions based on builder or contractor preferences. 

Overview of Sequential Search Technique 

The sequential search method used by the analysis method involves searching all categories (wall type, 

ceiling type, window glass type, HVAC type, etc.) for the most cost-effective option at each sequential 

point along the path to ZNE. Starting with the base case building, simulations are performed to evaluate all 

available options for improvement (one at a time) in the building envelope and equipment. Based on the 

results, the most cost-effective option 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. 

SAMPLE LEAST-COST SYSTEM OPTIMIZATION RESULTS 

Figure 3 shows sample optimization results for points that provide the least overall system costs as a 

function of source energy savings. The symbols indicate optimal building designs along the least-cost 

curve (at various levels of energy savings) found by the sequential search technique. 

Starting from the base case, total annual costs decrease, while energy savings increase. The initial rate 

of decrease in annual costs (i.e., the slope of the curve) is remarkably linear. No-cost options (such as 

window redistributions) lead to pure utility cost savings, which proceed along downward-sloping lines 

from the base case annual costs (y-axis intercepts) to the lower right corner of the graph (zero utility bill 

cost, not including hook up charges and fees, at 100% energy savings). 

5 
The final straight-line part of the curve corresponds to the incremental cost of using residential PV to 

offset the remaining energy provided by gas and electric utilities. The slope is proportional to the per Watt 

cost of PV and inversely proportional to the solar radiation. Total Annual Cost ($/year)

Source Energy Savings (%)

0

260

520

780

1040

1300

1560

1820

2080

2340

2600

0 10 20 30 40 50 60 70 80 90 100

Total Annual Cost ($/year)

Source Energy Savings (%)

0

260

520

780

1040

1300

1560

1820

2080

2340

2600

0 10 20 30 40 50 60 70 80 90 100

Figure 3. Sample system optimization results showing least-cost system curve 

A close-up view of all of the points considered by the sequential search in Figure 3 is shown in Figure 

4. Each symbol represents a particular simulation in the optimization search with different search iterations 

indicated by different colors. The analysis method allows the user to step through the results one iteration at 

a time to see how the optimization progresses. The user can also zoom in, select individual points, display 

associated building characteristics, and evaluate alternative building designs. 

It is important to emphasize that the points on the least-cost curve represent the potential performance 

that can be achieved by homes that are fully optimized with respect to energy cost performance. The leastcost curve cannot be used as a predictor of actual costs for homes that lie off the least-cost curve. Total Annual Cost ($/year)

Source Energy Savings (%)

559

679

800

921

1041

1162

1283

1403

1524

1645

1766

0 7 15 22 29 37 44 51 59 66 73

Total Annual Cost ($/year)

Source Energy Savings (%)

559

679

800

921

1041

1162

1283

1403

1524

1645

1766

0 7 15 22 29 37 44 51 59 66 73

Figure 4. Close-up of sample optimization results showing all points in the 

neighborhood of the least-cost curve 

6 
OVERVIEW OF INPUTS FOR THIS STUDY 

The analysis method used in this paper can include any system option or component whose 

performance can be defined in the context of TRNSYS or DOE2 energy-simulation programs and for 

which first costs, installation costs, O&M costs, and replacement costs can be specified over a 30-year life. 

As in any analysis, the results are subject to the assumptions used during the study. For the purposes of 

evaluating cost performance tradeoffs for near-term project energy performance targets, costs and 

performance for a range of currently available building materials and components were used in this study. 

Building Characteristics Considered in This Study 

A simple two-story 1,800-ft2

 residential building with an attached two-car garage was used for this 

study with the front of the building facing west. The building is modeled with climate appropriate 

foundations (e.g., an unconditioned basement in cold climates like Chicago, slab on grade in warm climates 

like Phoenix). The building has 2-ft eaves. Window area is assumed to be 18% of floor area and is equally 

distributed between outside walls. Adjacent buildings 10 ft to the north and south provide shading of 

sidewalls. The study was limited to a worst case (west) orientation. The impacts of other orientations will 

be considered in future studies. The energy options considered in the study include space conditioning 

systems (up to SEER 14 in the current study), envelope systems, hot water systems, lighting systems, major 

appliances, and residential PV. No options that contribute to miscellaneous electric loads other than major 

appliances were included in the study. The homeowner costs calculated in the study assume a 30 year 

mortgage at a 7% interest rate with a 3% general inflation rate and a 5% discount rate. No maintenance 

costs were included in the study. The specific descriptions of the design options considered in this study 

cannot be included within the space limitation for this paper and are included in the appendix of Anderson 

et al. 2004.

Occupancy/Operational Assumptions 

Occupancy and operational assumptions are as defined in the Research Benchmark (Hendron 2005) 

and include time-of-day profiles for occupancy, appliance and plug loads, lighting, domestic hot water use, 

ventilation, and thermostat settings. 

Base Case Building 

Results are calculated relative to a base case building for each climate. Base case buildings are defined 

in the Research Benchmark, including wall, ceiling, and foundation insulation levels and framing factors, 

window areas, U-values and solar heat gain factors, interior shading, overhangs, air infiltration rates, duct 

characteristics, and heating, cooling, and domestic hot water system efficiencies (Hendron 2005). 

Cost Assumptions 

Each option has an assumed first cost and lifetime. Costs are retail and include national average 

estimated costs for hardware, installation labor, overhead, and profit. Some are input as unit costs that are 

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., cost of solar water 

heating systems). Inputs can also be based on total costs (e.g., 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 typically based 

on national average cost data (R. S. Means 1999). Window and HVAC costs are based on quotes from 

manufacturers’ distributors. Appliance costs are based on manufacturers’ suggested retail prices. 

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) are modeled based 

on cumulative hours of use. 

Utility costs are assumed to escalate at the rate of inflation (i.e., to be constant in real terms). The 

mortgage interest rate is 5% above the rate of inflation. 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 

7 
O&M costs3

. This cost is assumed to be independent of PV system size. Additional costs associated with 

mounting large PV arrays were not considered. Natural gas is assumed to cost $1/Therm in all locations. 

Because of the wide variation in electric cost, local electric costs were used for each city (Table 1). 

Table 1. Local Electric Costs Used in Study 

City Electric Cost ($/kWh) 

Atlanta 0.0554 

Chicago 0.08275 

Houston 0.117 

Phoenix 0.081 

San Francisco 0.126 

The cost estimates used in this study do not include the initial costs required to re-engineer home 

designs4

, state and local financial incentives and rebates, or hidden costs, such as warranty and call-back 

costs that are not already accounted for as part of the O&M costs for the option. All of these additional cost 

factors can have a significant impact on builder business decisions related to implementation of new system 

designs. 

OVERVIEW OF REQUIREMENTS FOR ZERO NET ENERGY HOMES IN FIVE CLIMATE 

ZONES 

To provide an overall assessment of differences between climates, system optimizations were run for 

five cities (Atlanta, Chicago, Houston, Phoenix, and San Francisco). These cities correspond to the mixedhumid, cold, hot-humid, hot dry, and marine climates, targetted by the research program (Figure 5). 

Least-Cost Path to Zero Net Energy 

Figure 6 shows least-cost system curves for a new single-family home in the five cities considered in 

the study. The y-axis shows energy-related costs, including both utility bills and mortgage payments for 

energy options. The x-axis shows percent energy savings relative to the Benchmark house definition. Out 

of all of the different possible combinations of options considered in the sequential search process, the 

points shown in Figure 6 are the least-cost solutions for the west facing orientation. For the Benchmark 

buildings (at x = 0), annual costs are highest in Houston and lowest in Atlanta. 

 

3

 This price may not be currently available in all markets. The DOE Solar Program reports that current 

residential system PV costs (without including subsidies and O&M costs) are about $9/W. The Solar 

Program goal is to reduce base residential system costs from $9/W to $5.25/W by 2007 and $2.80/W by 

2020 [10]. 

4

 Re-engineering costs include market surveys to evaluate the potential to recover increases in home costs, 

costs associated with renegotiating relationships with suppliers and contractors, costs required to advertise 

new home features, technical support required to pass code review of new and innovative systems, and 

costs for contractor training. These costs are largest for early adopters and market leaders who are among 

the first to try new systems and are proportionally smaller for best practice builders and standard practice 

builders who wait before adopting new systems. 

8 
Figure 5. Building America climate zone map 

Mortgage + Utilities ($/yr)

 

Source Energy Savings (%/yr)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 10 20 30 40 50 60 70 80 90 100

Atlanta 

Phoenix

Chicago

Houston

San Francisco

Figure 6. Least-cost curves for five cities 

9 
 In all cities, total annual costs decrease while energy savings increase starting from the Benchmark. 

The initial rate of decrease in annual cost versus energy savings (i.e., the slope of the curves) out to the 

30% savings point is similar for all five climates. There are several reasons for this similarity. First, the 

initial slope is set by low cost options with pure utility cost savings, which proceed along downwardsloping lines from the base case annual costs (y-axis intercepts) to the lower right corner of the graph (zero 

utility bill cost at 100% energy savings). Second, some of the savings are a result of options, such as 

lighting and appliances, where savings are only weakly climate dependent. Potential cost savings are 

somewhat less in Atlanta than in other locations because of low energy use and low electric rates. 

The minimum cost points occur at approximately 30% for Atlanta, San Francisco, and Chicago and 

40% for Houston, and Phoenix. The present value of investments in improved energy efficiency required to 

operate in the minimum cost area of the curves are summarized in Table 2. 

All cost curves are fairly flat out to about 40% and then begin to rise with the exception of Phoenix, 

where costs don’t begin to rise until 50%. The crossover point where investment shifts from energy 

efficiency to onsite power occurs between 40% (San Francisco) and 50% (Phoenix) depending on climate. 

The combination of low annual energy use and high electric rates in San Francisco and high annual energy 

use and low electric rates in Phoenix account for the large difference in the location of the crossover points 

for these two cities. The final straight-line parts of the curves correspond to the cost of onsite power 

provided by PV to achieve 100% energy savings. 

Recommended Investments in Efficiency for Homes with Integrated Onsite Power Systems 

Figure 7 shows the present value of energy efficiency costs at the point where the marginal cost of 

increasing energy efficiency equals the cost of adding PV. These are the investments in energy efficiency 

that would be recommended from a least-cost perspective before making investments in PV systems. The 

recommended investment in energy efficiency upgrades varies by nearly a factor of two from $8,432 in San 

Francisco to $15,166 in Chicago. The PV capacities required to achieve ZNE for the 1,800-ft2

 home 

considered in this study and the corresponding energy savings at the crossover point from investment in 

energy efficiency to investment in onsite power are shown in Table 3. 

Table 2. Investment Required to Achieve Minimum Energy Cost 

Location % Whole House Energy 

Savings at Minimum in 

Least-Cost Curve 

Corresponding Present Value 

of Investment in New Home 

Energy Efficiency 

Atlanta 32% $1749 

Chicago 28% $3899 

Houston 38% $2585 

Phoenix 39% $2585 

San Francisco 27% $1337 

10 
Figure 7. Present value of efficiency options ($) at the crossover point for investment in onsite power 

 

 ZNE Option Cost (present $'s, from Base)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Chicago-W

($15,166)

4800

Houston-W

($8,762)

4800

Atlanta-W

($15,166)

4800

Phoenix-W

($9,553)

4800

San Fran-W

($8,432)

4800

Building

Orientation

Neighbors

Total Window Area

Envelope

Walls

Ceiling

Foundation

Thermal Mass

Infiltration

Windows & Shading

Glass Type

Window Area per Wall

Eaves

Appliances & Lighting

Refrigerator

Dishwasher

Washer/Dryer

Lighting

Equipment

HVAC

Ducts

Water Heater

Other

Renewables

Solar DHW

Table 3. PV Capacities Required to Achieve ZNE, Assuming Maximum 

Cost-Effective Investment in Energy Efficiency 

Location Crossover Point PV Capacity Required to 

Achieve ZNE (kW) 

Atlanta 49% 5.6 

Chicago 46% 7.6 

Houston 51% 6.2 

Phoenix 52% 5.4 

San Francisco 43% 4.8 

Table 4. Cost Multiplier Required to Reach ZNE Relative to Minimum Cost Point 

Location 

Minimum 

Cost ($) 

ZNE Cost 

($) Ratio 

Atlanta 1,749 52,351 30 

Chicago 3,899 71,874 18 

Houston 2,585 56,759 22 

Phoenix 2,585 49,679 19 

San Francisco 1,337 42,808 32 

11 
12 

Based on a review of the location of the crossover points shown in Table 3 and the cost ratios shown in 

Table 4, additional residential building components will be required to cost effectively meet whole-house 

residential-building energy-performance goals beyond the year 2010. Additional efficiency improvements 

in space-conditioning systems, hot-water systems, lighting systems and major appliances are not likely to 

be sufficient by themselves. Development of cost-effective solutions for miscellaneous electric loads and 

research leading to significant reductions in the cost of onsite power systems will also be needed. 

Establishing specifications for the advanced components needed to meet future energy performance goals 

will be an important research activity for the project over the next several years. 

DESIGN/TECHNOLOGY OPTIONS FOR 40% ENERGY SAVINGS IN FIVE CLIMATES 

Figures 8-12 provide a summary of the least cost design/technology options required to achieve 40% 

energy savings in each city. The incremental cost of the last step required to reach 40% is highlighted in 

with a black arrow on the right-hand side of the figures. The minimum investments required to reach 40% 

energy savings are summarized in Table 5. The cold climate (Chicago) is the most expensive climate 

followed by the marine climate (San Francisco). It is more costly to reduce energy use in climates 

dominated by heating than in climates dominated by cooling. Table 5 also includes the costs required to 

reach 50% savings without investing in onsite power. In the context of the current study with a base onsite 

power cost of $7.50/W, no system solutions were found that could cost effectively reach 50% savings in 

Chicago or San Francisco without the use of PV. 

The specific results shown in Figures 8-12 are subject to the options and assumptions included in the 

present study and are representative of energy savings and costs that can be achieved after the house reengineering process has been completed and homes are offered on a production basis. The final system 

solution chosen by a specific builder will depend on his design objectives, his target market, his assessment 

of the reliability and constructability of different system options, and the level of technical support for 

system design changes and quality control that he receives from his suppliers and contractors. Colored 

points showing other combinations of efficiency options are included in Figures 8-12 to show the additional 

system solutions that are available in the near neighborhood of the least-cost curve. Optoins within each 

category are arranged in terms of increasing cost. The shaded portion of the bars on the right hand side of 

Figures 8-12 represent the fraction of all possible options within each category that have been considered at 

each savings level. 
Figure 8. Atlanta 40% savings point 

13 
Figure 9. Chicago 40% savings point 

14 
Figure 10. Houston 40% savings point 

15 
Figure 11. Phoenix 40% savings point 

16 
17 

Figure 12. San Francisco 40% savings point 
Table 5. Minimum Investment Required for Reaching 

40%-50% Savings without Onsite Power 

Location Minimum Cost at 

40% Point ($) 

Minimum Cost at 

50% Point ($) 

Atlanta 4,386 11,452 

Chicago 8,261 NA 

Houston 3,244 9, 896 

Phoenix 3,244 7,646 

San Francisco 5,538 NA 

CONCLUSIONS 

The sequential search technique used in the analysis method efficiently identifies the least-cost 

approach to whole-house energy performance goals based on evaluation of thousands of annual hourly 

energy simulations involving different combinations of discrete residential system equipment and material 

options. The sequential search technique utilized in this paper has several advantages. First, it finds 

intermediate optimal points all along the least-cost curve (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 to reflect realistic construction options. Third, near-optimal 

designs in the neighborhood of the least-cost curve are identified and retained as alternative solutions 

depending upon builder and consumer preferences. In addition to simply searching for the sequence of 

optimal improvements in building design along the least-cost curve, the analysis method also handles 

special cases with negative interactions: (1) removing previously selected options and (2) re-evaluating 

previously rejected combinations of options. 

The initial analysis presented in this paper has identified the energy related system components and 

costs required to achieve 40-50% savings levels relative to the Benchmark. Using current component/cost 

assumptions and assuming no reduction in the use of energy for miscellaneous electric loads other than 

major appliances, the crossover point on the least-cost curve from energy efficiency to onsite power is 

projected to occur between the 50% and 60% whole-house energy-savings level. 

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. 

ACKNOWLEDGMENTS 

This work was supported by the U.S. Department of Energy, Office of Building Technologies. The 

support and leadership of Ed Pollock, George James, and Lew Pratsch of the DOE Building America 

Program, is gratefully acknowledged. We also thank Bob Hendron, Paul Norton, Mark Eastment, Jay 

Burch, and Ron Judkoff of NREL’s Center for Buildings and Thermal Systems, along with Ed Hancock, 

Paul Reeves and Blaise Stoltenberg for their interest and valuable discussions on the topic of this paper. 

This work would not have been possible without the continuing research efforts, feedback, and problemsolving contributions of Building America’s industry teams who have excelled at finding system solutions 

on the least-cost curve. 

REFERENCES 

Anderson, R., C. Christensen, G. Barker, S. Horowitz, A. Courtney, T. Givler and K. Tupper. 2004. 

Analysis of System Strategies Targeting Near-Term Building America Energy-Performance Goals for 

New Single-Family Homes: FY 2004 Fourth-Quarter Building America Milestone Report. NREL/TP550-36920. Golden, CO: National Renewable Energy Laboratory. 

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. 

18 
Christensen, C., G. Barker and B. Stoltenberg. 2003a. "An Optimization Methodology for Buildings on the 

Path to Zero Net Energy." Proceedings of the Solar 2003. Austin, TX: American Solar Energy Society. 

Christensen, C., G. Barker and B. Stoltenberg. 2003b. "An Optimization Method for Zero Net Energy 

Buildings." Proceedings of the International Solar Energy Conference. Kohala Coast, HI: American 

Society of Mechanical Engineers. 

Davis Energy Group. 1993. ACT2 Stanford Ranch Site, Final Design Report. Davis, CA: Davis Energy 

Group. 

Florida Solar Energy Center. 2005. EnergyGauge Pro. Cocoa, FL: Florida Solar Energy Center 

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

Hendron, R. 2005. Building America Research Benchmark Definition, Updated December 29, 2004. 

NREL/TP-550-37529. Golden, CO: National Renewable Energy Laboratory. 

Hendron, R., R. Anderson, R. Judkoff, C. Christensen, M. Eastment, P. Norton, P. Reeves and E. Hancock. 

2004. Building America Performance Analysis Procedures: Revision 1. NREL/TP-550-35567. Golden, 

CO: National Renewable Energy Laboratory. 

RSMeans. 1999. Residential Cost Data- 18th Annual Edition. Kingston, MA: RSMeans, Company, Inc. 

Wetter, M. “GenOpt®, "Generic Optimization Program,” Seventh International IBPSA Conference. Rio de 

Janeiro, Brazil (http://www.ibpsa.org/bs_01.htm). 

19 
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Analysis of Residential System Strategies Targeting Least-Cost 

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The U. S. Department of Energy’s Building America residential systems research project uses an analysis-based system 

research approach to identify research priorities, identify technology gaps and opportunities, establish a consistent basis 

to track research progress, and identify system solutions that are most likely to succeed as the initial targets for 

residential system research projects. 

This report describes the analysis approach used by the program to determine the most cost-effective pathways to 

achieve whole-house energy-savings goals. This report also provides an overview of design/technology strategies 

leading to net zero energy buildings as the basis for analysis of future residential system performance. 

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Building America; U.S. Department of Energy; zero energy homes; analysis-based systems research; whole-house 

energy savings; energy efficient homes 

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