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Contract No. DE-AC36-08GO28308

Simplifying Energy Efficiency for 

Homeowners – Results from a 

California Utility’s Pilot Study of a 

Scalable Solution to Assess Retrofit 

and Solar Opportunities

Preprint

Mudit Saxena, Brian Coffey, and Peter May-Ostendorp

Vistar Energy

Joshua Rasin

Sacramento Municipal Utility District (SMUD)

Eric Wilson and Scott Horowitz

National Renewable Energy Laboratory

Presented at the 2018 ACEEE Summer Study on Energy 

Efficiency in Buildings

Pacific Grove, California

August 12–17, 2018

Suggested Citation

Saxena, Mudit, Brian Coffey, Peter May-Ostendorp, Joshua Rasin, Eric Wilson, and 

Scott Horowitz. 2018. “Simplifying Energy Efficiency for Homeowners – Results from a 

California Utility’s Pilot Study of a Scalable Solution to Assess Retrofit and Solar 

Opportunities: Preprint.” Golden, CO: 

National Renewable Energy Laboratory. NREL/CP-5500-72176. 

https://www.nrel.gov/docs/fy19osti/72176.pdf.

Conference Paper

NREL/CP-5500-72176

October 2018
NOTICE

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provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Buildings 

Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the 

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acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish 

or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content.
Simplifying Energy Efficiency for Homeowners – Results from a 

California Utility’s Pilot Study of a Scalable Solution to Assess Retrofit and 

Solar Opportunities

Mudit Saxena, Brian Coffey, Peter May-Ostendorp, Vistar Energy

Joshua Rasin, Sacramento Municipal Utility District (SMUD)

Eric Wilson, Scott Horowitz, National Renewal Energy Laboratory (NREL)

ABSTRACT

For homeowners, typical choices to reduce net energy use in their homes include:

upgrading their home with a tailored efficiency retrofit or investing in roof-top solar. 

Determining the unique combination of these options that will provide the most cost-effective 

solution requires analysis for each home. This analysis needs to be presented in a simple and 

actionable way to homeowners. The solution should also be easily scalable to thousands of 

homes to enable utilities to better target efficiency program options and support solar 

installations.

Sacramento Municipal Utility District (SMUD) piloted a new tool developed by Vistar 

Energy called XeroHomeTM which automates the assessment of energy efficiency and solar 

opportunities in existing homes. XeroHome demonstrates an approach to using building energy 

modeling, energy use data analytics and homeowner engagement to identify and target energy 

efficiency opportunities at scale.

XeroHome uses automation to build an EnergyPlusTM model for each home, based on 

information from public data sources such as property assessment records, building permit

records, aerial/satellite photography, and template models derived from ResStockTM. Utilizing 

cloud computing, machine learning and optimization algorithms, energy upgrade packages are 

ranked by cost-effectiveness for each home and presented over an interactive web-portal.

This paper discusses SMUD’s pilot project using XeroHome to conduct analysis for 

1,500 existing homes in Sacramento, CA. Development of XeroHome’s advanced analytics 

capabilities are discussed in the context of applicability for utility programs.

Opportunities and Obstacles in Residential Energy Retrofits

According to a recent study on the U.S. building energy efficiency market size

(Rockefeller Foundation and Deutsche Bank CCA 2012), existing residential buildings represent 

a staggering 80% of the total U.S. building floor area. Further, about 50% of the energy savings 

potential from the entire building sector can come from single-family residential buildings alone 

(Figure 1). Another study by NREL (Wilson, et al. 2017) estimates the economically achievable

energy savings potential from single-family residential buildings in U.S. to be 245 terawatt-hours 

(TWh), which amounts to 6.3% of total U.S. electricity consumption. This makes single-family 

homes a prime opportunity for energy efficiency programs nationwide. However, the diversity 

and fragmentation in the single-family residential segment makes it difficult to develop and 

promote energy efficiency programs at scale.

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Figure 1: US Building Stock Analysis (Rockefeller Foundation and Deutsche Bank CCA 2012)

Understanding which energy efficiency measures will be effective for a specific home, or 

for groups of homes, can take time, and requires detailed analysis. Whole-house energy 

improvement programs have traditionally relied on detailed on-site energy audits to identify 

energy upgrade opportunities. However, that approach takes time, requires specialized skills, and 

importantly – cannot be scaled quickly to large geographical areas. A simpler but much less 

accurate alternative that is sometimes used, is to rely on crude assumptions based on home age 

and location for identifying retrofit opportunities. Utilities, local governments, and technology 

vendors that need this targeting information for their programs and products are left with 

essentially two unappealing alternatives. One provides sufficient rigor in analysis, but is 

expensive, labor-intensive and does not scale well, while the other is defeated by the lack of 

detail in oversimplified assumptions.

Building Energy Modeling Based Assessments for Existing Homes

Building energy modeling (BEM), using a whole building energy simulation tool like

EnergyPlus, can be an effective approach to providing accurate and reliable assessment of energy 

retrofit and solar opportunities for existing homes, provided that quality information is used to 

construct the models (avoiding “garbage in, garbage out”). In the past, this proviso has generally 

required on-site energy audits to inform the modeling. However, we are now entering an age of 

data availability that makes the process plausibly automated.

An alternate approach has been to use utility billing analysis and data analytics with 

smart meter (or AMI) data to gain insights into existing energy use patterns. This approach can 

provide some degree of end-use data disaggregation and is relatively easier to scale for the 

existing building stock. While the approach has been shown to be useful in detecting certain enduses with high energy use, with varying degrees of certainty, its usefulness in evaluating costeffective energy efficiency retrofit and solar alternatives is notably limited (Kupser, et al. 2016). 

This is mainly due to the lack of an energy model in which alternate efficiency measures and 

packages of measures can be simulated. The approach is most suited to development of 

behavioral programs and providing measurement and verification applications that track changes 

in pre and post-retrofit performance.

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BEM tools such as BEoptTM and more recently OpenStudio®, from National Renewable 

Energy Laboratory (NREL), have simplified and expanded usability of EnergyPlus as a 

simulation tool for existing buildings. More recently NREL developed a large-scale simulationbased residential energy analysis tool called ResStock, that provides highly granular modeling of 

the U.S. housing stock. Using advanced statistical sampling techniques, the ResStock team

developed a comprehensive set of EnergyPlus template models that represent the vast variety of 

age, size, construction practices, and equipment in the U.S. housing stock. The resulting data set

has provided deep insights into potential savings opportunities across multiple states and regions 

in the U.S. (Wilson, et al. 2017). This dataset can be used by policy makers, program designers

and manufacturers to identify cost-effective improvements in a particular state or region, as well 

as help identify customer segments for targeted marketing and deployment.

XeroHome

The recent developments in batch processing and parametric analysis using EnergyPlus, 

OpenStudio, and ResStock’s development of template models, have opened many possibilities 

for BEM-based analysis of existing homes. XeroHome is a targeting and outreach tool developed 

by Vistar Energy that leverages these advances to address the needs of targeting and engagement 

for utility programs. 

XeroHome uses automation to build EnergyPlus models for individual homes and find 

the most cost-effective way to reduce energy use for a home. The energy models are developed

based on information about a home from public data sources, supplemented initially by reasoned 

assumptions. Data sources such as property assessment and building permit records provide key 

data on when the home was built and dates when major components of the home were upgraded. 

These are then cross-referenced with expected efficiencies of various components of the home, 

based on historic energy codes, which form the inputs for the base energy model. Further, 

aerial/satellite photography and remote sensing data is used to develop a 3D model, complete 

with attributes such as tree shading and window areas. Utility bill data, if available, is used to 

calibrate the energy model. Energy model annual simulation outputs and energy efficiency 

measure (EEM) cost data are then used to find the most cost-effective way to use retrofits and 

solar PV installations to lower energy use and ultimately reach annual zero-net energy (ZNE). 

This information is presented as a “Path to Zero-Net Energy” that starts with the current building 

and ends with a zero-net energy version of the building. Homeowners can pick any point inbetween as their ending point. 

This whole process is completely automated, which allows XeroHome to scale the 

development of energy models for homes rapidly. XeroHome employs a number of innovative 

techniques such as machine learning to perform the EEM-selection optimization in a 

computationally tractable and scalable manner. XeroHome also extensively uses distributed 

cloud computing that compresses the time required to return results. Analysis using the energy 

models provides the preliminary – also called “white-level” – results, which are presented over 

an interactive web-portal to a homeowner. 

Leveraging Homeowner Engagement to Increase XeroHome’s Analysis Accuracy 

A central feature of XeroHome’s design is its engagement with the homeowner. Through 

XeroHome’s web-based interface, a homeowner will initially be able to access their home’s 

‘white-level’ analysis results using just their home address. The entire set of upgrade 

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recommendations are provided to them in the form of an easy-to-follow “Path to Zero-Net 

Energy”. The path is a visually engaging and interactive way to convey key information to 

homeowners about which energy retrofit options maximize their home energy improvement 

investment.

Leveraging this engagement with the homeowner, XeroHome provides a way to 

incrementally increase accuracy and reduce the uncertainty of its analysis. It provides

homeowners, as well as professional energy auditors, a means to enter on-site observations and 

energy audit data for a home. As users enter this data, it is used internally to replace most of the 

assumed inputs in the home’s ‘white level’ analysis energy model. The energy simulations are 

then re-run to update results. As more on-site observed or measured data is provided, uncertainty 

around the initial ‘white level’ analysis decreases. This is conveyed to the homeowner in terms 

of progressing colored badges associated with the analysis. The colors indicate increasing level 

of accuracy – going from an initial ‘white level’, to ‘green level’ and ultimately a ‘blue level’

analysis. To achieve a ‘blue badge’ would require detailed data on multiple inputs, which will 

likely require a professional energy audit in most homes.

To aid this process, XeroHome creates a prioritized list of questions, based on the relative 

ease of collection and value of that piece of information in decreasing the uncertainty in the 

results. This unique step allows users to focus data collection efforts on the information that is 

more likely to impact the EEM recommendations. The questions and badges also introduce a 

level of “gamification” to the process of data collection and survey, which otherwise is thought 

of as a barrier for such applications. 

Achieving this interaction and engagement from homeowners is an important attribute of 

the XeroHome tool. As a tool for utilities that want to increase program participation and connect 

with customers, XeroHome provides a platform for finding and engaging with those customers. 

Since these are homeowners that are already well-informed and engaged, they are also more 

likely to act.

SMUD Pilot with XeroHome

Sacramento Municipal Utility District (SMUD) piloted XeroHome’s approach to 

assessing energy retrofit and solar opportunities for 1,500 single-family residential customers in 

the North Franklin community in Sacramento, CA. For these customers, which represent a 

disadvantaged, low-income community, SMUD needed energy evaluations to determine which 

of its program offerings were best suited to individual homes. SMUD offers rebate programs for 

several energy upgrades as well as a whole-house bundled approach with its Home Performance 

Program. SMUD also offers a community solar program called SolarShares that offers its

customers the opportunity to access solar power without upfront costs or equipment installation.

SMUD had a particular goal to determine which energy efficiency opportunities were 

most cost effective for these homes, and the best opportunities to reduce customer bills while 

increasing access to solar energy, particularly participation in the SolarShares program.

Utilizing the XeroHome tool, the analysis focused on answering these two key research 

questions:

1. What is the potential energy savings from cost-effective energy efficiency measures 

(EEMs) offered by SMUD residential programs for these 1,500 homes?

2. Within the 1,500 homes, which EEMs show up most often as being cost effective? Are 

there EEMs that SMUD’s Programs should be offering that they currently do not? 

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Data Acquisition for North Franklin Community

The area of North Franklin in Sacramento is a 2.3 square mile stretch of residential area, 

located south of downtown Sacramento. To begin the process of analyzing these homes, 

XeroHome acquired property assessment data from the Sacramento County assessor’s office and 

building permit records from the City of Sacramento for the 1,500 single-family homes. 

From the county records two key pieces of information obtained were: Each home’s year 

of construction (year built) and size (area in square feet). A distribution of the homes’ year built 

and home size is provided in Figure 2. The distribution shows that most homes in this 

community were built between 1940 – 1960, and are between 750 sf to 1,500 sf. 

Figure 2: Distribution of Homes - Year Built and Home Size Area

Building permit records provide a unique insight into major changes that a home has 

undergone such as re-roofing, window replacement, HVAC or water heater change outs, etc. Of 

the 1,500 homes in North Franklin community, about 70% of the homes had at least one building 

permit on record. The building permit data was divided into 11 categories. Figure 3 shows the 

percent of homes with building permits for each category. 

The property assessment data from county records and building permits were then used to 

look up expected efficiencies of various components of each home, based on historic California 

Title 24 energy codes. The expected efficiency provides a close approximation of what may have 

been installed when the home was built or when its components changed.

YEAR BUILT HOME SIZE (AREA IN SF) 

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Figure 3: Percent of Homes with Building Permits for Each Category

The energy models were refined using 3D geometry and local shading from 

aerial/satellite imagery and 3D remote sensing tools acquired from various commercial data 

sources. As shown in Figure 4, the extracted home models have a high enough level of detail to 

determine wall and roof areas and their orientations. XeroHome uses this data to generate a 3D 

model in EnergyPlus for each home, adding contextual shading from trees, as shown in Figure 5. 

Figure 4: 3D Geometry for Homes from Remote Sensing and Satellite/Aerial Imagery Datasets

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Figure 5: 3D Model in EnergyPlus - Tree as Shade Element Modeled Behind the Home 

Base Model Development and Parametric Simulation

With the 3D geometry and various component efficiencies in place, XeroHome 

completes the EnergyPlus model using reasoned assumptions for the remaining inputs. A wellthought out set of default assumptions have been developed for any input parameter that does not 

have a reliable data source, such as occupant schedules, plug load profiles, equipment set points 

etc. For this XeroHome leverages the extensive work done by NREL on the development of 

ResStock and its residential template EnergyPlus models, that are designed to represent homes of 

certain vintages and geographical locations (Wilson, et al. 2017). 

Given a base building model and a set of possible EEMs, XeroHome then runs 

parametric simulations and optimization to develop a “Path to Zero-Net Energy” for each of the 

1,500 homes. The optimization problem is similar to that faced by NREL’s BEopt tool. The 

BEopt model (Horowitz, et al. 2008) provides some precedence and grounding to that of 

XeroHome’s. However, there are two constraints that are unique to XeroHome: The need for fast 

(almost real-time) execution of the optimization step to support homeowner engagement over its 

web-portal, and scalability to present analysis for multiple homes. To accomplish this, several 

innovations have been applied including the use of machine learning to develop a fast-running 

approximation to the EnergyPlus model for each building. This approach has shown to increase 

computational efficiency, providing near-real-time capabilities, and is more effective at rapid 

scaling of the optimization process from one to multiple homes.

Parametric Analysis

In the parametric analysis step, XeroHome applies various combinations of 12 EEMs and 

one Roof-Top Solar measure to the energy models. These 12 measures represent typical singlefamily retrofit options available to homeowners. They covered measures offered by SMUD’s 

Home Performance Program, as well as a few not currently provided by SMUD’s program. 

For each EEM, XeroHome models a range of efficiency options that cover products 

available in the market (see Table 1). XeroHome is unique in its use of EnergyPlus for its base 

model simulations, which allows it to model EEMs with a high level of accuracy to represent the 

differences in efficiencies of actual products. For example, XeroHome models the EEM of 

‘Improve Ceiling Insulation’ for a range from U-factor 0.227 to 0.016 Btu/hr-sf-F. Within this 

range are 8 efficiency options ranging from R-0 un-insulated wood frame attic to R-60 with 

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fiberglass insulation. The parametric analysis considers all possible options for a home - going 

from its initial base condition, to higher efficiency option for each component.

Table 1: Summary of Energy Efficiency Measures Modeled

Results from XeroHome Analysis of North Franklin Community

The analysis form XeroHome provides insight into the retrofit opportunities for homes in 

the North Franklin community. In this phase of the work, the analysis completed for the 1,500 

homes, was a “white-level” analysis, in that the analysis was not updated for individual homes 

with audit data or on-site observations. Rather, the models were developed based on data 

acquired from county records of property assessments, building permits and 3D geometry as 

described earlier. The goal of the analysis was to answer key research questions for SMUD, as 

discussed earlier.

Most Common Cost-Effective EEMs

From the full set of analysis for all 1,500 homes, EEMs and roof-top solar 

recommendations from each home’s “Path to Zero-Net Energy” were ranked by pay-back period. 

EEMs with a payback period of less than or equal to 5 years were identified and the frequency 

with which they appear in our analysis set of 1,500 homes is shown in Figure 6. While analysis 

was done for other payback periods (of 10 and 15 years), for sake of brevity only results from 

payback period less than or equal to 5 years is presented in this section. To calculate payback, 

the cost, including purchase and installation of the measures, is divided by the first year of 

savings, both in present dollars. Costs for all the EEMs and the cost of energy were developed in 

close collaboration with SMUD to represent the cost for SMUD customers and the Sacramento, 

CA region. 

Replace Lighting Avg. lm/Watt - Range (15 - 80) 7

Replace Water Heater Energy Factor - Range (0.59 - 0.95) 5 0.227 4.4 Uninsulated, Vented

Weatherization ACH at 50Pa. Pressure (25-1) 12 0.091 11 Ceiling R-7 Fiberglass, Gr-1, Vented

Install Whole House Fan (Yes/No) 1 0.059 16.9 Ceiling R-13 Fiberglass, Gr-1, Vented

Improve Ceiling Ins. U-Factor Range (0.227 - 0.016) 8 0.044 22.9 Ceiling R-19 Fiberglass, Gr-1, Vented

Replace Furnace AFUE - Range (0.6 - 0.98) 12 0.029 33.9 Ceiling R-30 Fiberglass, Gr-1, Vented

Install Radiant Barrier (Yes/No) 1 0.024 41.9 Ceiling R-38 Fiberglass, Gr-1, Vented

Seal Ducts Duct leakage % - Range (30% - 7.5%) 5 0.019 52.9 Ceiling R-49 Fiberglass, Gr-1, Vented

Replace Central AC SEER - Range (8 - 24.5) 11 0.016 63.9 Ceiling R-60 Fiberglass, Gr-1, Vented

Insulate Ducts R-Value - Range (0 - 8) 4

Improve Wall Ins. U-Factor Range (0.25 - 0.065) 8

Replace Windows U-Factor Range (1.16 - 0.17) 25

Energy Efficiency Measure 

(EEM)

Efficiency Range 

(Min-Max)

Number of 

Efficiency 

Options 

Modeled

U-Factor 

(Btu/hr.s

f.F)

R Value 

(hr.sf.F/B

tu)

Improve Ceiling Ins.

Efficiency Options

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Figure 6: Frequency of Occurrence of Cost Effective EEMs

Figure 6 shows that 9 of the 13 measures considered were cost effective in at least one 

home in the analysis set. Four measures, namely ‘Replace Central AC’, ‘Replace Water Heater’ 

and ‘Install Whole House Fan’ and ‘Roof-Top Solar’ did not show a payback ≤ 5 years. For most

homes, more than one measure that had a payback ≤ 5 years. 

The analysis showed that the ‘Seal Ducts’ measure appeared more often than any other 

measure in the homes with payback ≤ 5 years. This is not surprising, since duct sealing can be 

relatively cheap, and can have sizable savings. This is followed by ‘Replace Lighting’, which 

like the seal ducts measure, is relatively low cost as it does not include labor costs for installation 

(as it is assumed to be done by the homeowner). These are followed by envelope measures of 

‘Improve Wall Insulation’, “Improve Ceiling Insulation’ and ‘Replace Windows’. Since most of 

the homes in this analysis set are older homes, with the majority built before energy codes 

existed in California (between 1940-1960 - see Figure 2), the initial conditions assume thermally 

inefficient walls, ceilings, and windows. The few exceptions were homes that had building 

permits showing upgraded envelope components. Thus, improving these components to even 

slightly better efficiency, results in substantial energy savings, giving them a payback ≤ 5 years. 

Interestingly, ‘Install Whole-House Fan’ measure did not show up in homes as having 

payback ≤ 5 years, and ‘Weatherization’ only for a few homes. This is a result of very low 

energy savings from these two measures (as discussed in the next section). ‘Roof-Top Solar’ also 

does not show up in homes having payback ≤ 5 years, as the high cost pushes its payback closer 

to 10 and 15 years. 

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Energy Cost Savings from All EEMs 

Figure 7 shows the annual energy savings range for all EEM across all 1,500 homes. This 

graphs represents energy savings regardless of the EEM’s payback. The bars represent 1

st and 3rd

quartile results while the end of the lines show Min and Max savings. Note that while Figure 7

shows energy savings from each EEM, the order in which the EEMs appear on the X-axis is 

taken from Figure 6. Together the two graphs can provide insight into the which EEMs will 

provide high savings and be cost effective for most homes in the analysis set. 

Figure 7: Savings Range for Each EEM

Figure 7 shows that besides ‘Roof-Top Solar’, the three envelope measures of ‘Improve 

Wall Insulation’, “Improve Ceiling Insulation’ and ‘Replace Windows’ show relatively high 

savings. As explained earlier, this result is indicative of low insulation levels for envelope 

components as the initial condition for the older (pre-energy code) homes that make up the 

majority of our analysis set. Also, the savings graph shows the low savings from 

‘Weatherization’ and ‘Install Whole House Fan’ measures, which result in those measures 

having Payback greater than 5 years.

10 This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications.
Ability to Drill-Down to Individual Home Analysis

While these macro-visualizations of frequency and savings range of cost-effective EEMs 

provides valuable insights that a utility program can use to focus outreach efforts and customize 

program offerings, XeroHome offers a unique opportunity to go deeper beyond this level of 

analysis. Since XeroHome’s analysis is done at the individual home level, results can be 

examined down to the individual home. This ability to drill down, gives a lot of power to a user 

to customize the analysis of that home. Through XeroHome’s web interface, a homeowner can 

access information about their home using just their home address. Here they can inform 

themselves about which measures make most sense for them to invest in, by reviewing their 

customized “Path to Zero-Net Energy”. Using the same interface, a homeowner (or an energy 

auditor) can tap into the energy model for that home and provide site-observed and measured 

data to make the assessment more accurate. As these changes are made to individual homes, the 

resulting macro-level analysis is automatically updated, to present more accurate data.

Conclusion

The SMUD XeroHome Pilot project demonstrated the ability of a BEM-based analytical 

tool to generate energy retrofit recommendations at scale, with speed and accuracy. XeroHome’s 

EnergyPlus based analysis approach, which can be rapidly scaled through innovative use of 

cloud computing, machine learning and optimization algorithms, provides a solution for tackling 

the diversity and fragmentation in the single-family residential sector. It provides utilities with a 

tool to address this barrier to the large-scale development and success of whole-home 

improvement and energy upgrade programs. 

XeroHome’s ability to generate analysis for multiple homes, and then drill down to an 

individual home to access its custom recommendations using a simple, easy-to-use interface is 

finally how XeroHome can simplify energy efficiency for homeowners. The platform also gives

market actors in the home energy retrofit industry – utility program administrators, technology 

vendors, and home energy audit professionals – access to the same data, which gives them a 

platform for finding and engaging with potential program participants and customers. As 

accurate data on energy efficiency opportunities becomes available freely for ALL homes, and to 

ALL market actors and decision makers, a self-sustaining residential energy efficiency 

marketplace can develop, which is needed to achieve the eventual goal to a lower-carbon and 

low-energy residential sector.

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