Rooftop Solar TechnicalPotential for Low-to-Moderate
Income Households in the
United States
Benjamin Sigrin and Meghan Mooney
National Renewable Energy Laboratory

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Technical Report
NREL/TP-6A20-70901
April 2018

Contract No. DE-AC36-08GO28308

Rooftop Solar TechnicalPotential for Low-to-Moderate
Income Households in the
United States
Benjamin Sigrin and Meghan Mooney
National Renewable Energy Laboratory

Suggested Citation
Sigrin, Ben, and Mooney, Meghan. 2018. Rooftop Solar TechnicalPotential for Low-to-Moderate Income Households in the United States.
Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A2070901. https://www.nrel.gov/docs/fy18osti/70901.pdf.

NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
National Renewable Energy Laboratory
15013 Denver West Parkway
Golden, CO 80401
303-275-3000 • www.nrel.gov

Technical Report
NREL/TP-6A20-70901
April 2018

Contract No. DE-AC36-08GO28308

NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither
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Acknowledgements
This work was funded by the Solar Energy Technologies Office of the U.S. Department ofEnergy’s Office of Energy Efficiency and Renewable Energy under contract number DE-AC3608GO28308. The authors thank Zach Franklin, Nichole Hanus, Jessica Lin, Seungwook Ma,
Robert Margolis, Caleb Phillips, Tony Reames, Emma Tome, and Kim Wolske for their reviews
and input. We thank Evan Rosenleib and Anand Govindarajan (NREL) for their assistance inprocessing data. Lastly, we thank Nichole Hanus (Carnegie Mellon University) for providing uswith the geocoded school addresses. Opinions represented in this article are the authors’ own anddo not reflect the view of the U.S. Department of Energy or the U.S. government. Any errors oromissions are the sole responsibility of the authors.

1
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Acronyms
ACS
ANOVA
AMI
CARE
CBECS
CDBG
CPUC
DHS
DOE / U.S. DOE
EERE
EIA
FEMA
FMR
FPL
GWh
GIS
HHS
HSIP
HUD
KDE
kWh
LEAD
LiDAR
LIHEAP
LMI
MFOO
MFRO
NCES
NHGIS
NHPD
NLCD
NREL
NSRDB
OSM
PUMS
PV
RECS
ReEDS
REPLICA
reV
SFOO
SFRO
TMY

U.S. Census Bureau American Community Survey
Analysis of variancearea median income
California Alternate Rates for Energy
Commercial Buildings Energy Consumption Survey
Community Development Block Grant
California Public Utility Commission
U.S. Department of Homeland Security
U.S. Department of Energy
U.S. Department of Energy, Office of Energy Efficiency and
Renewable Energy
Energy Information Administration
Federal Emergency Management Agency
fair market rent
federal poverty levelgigawatt-hourgeographic information system
U.S. Department of Health and Human Services
Homeland Security Infrastructure Program
U.S. Department of Housing and Urban Development
Kernel Density Estimation
kilowatt-hour
Low-Income Energy Affordability Data
light detection and ranging
Low Income Home Energy Assistance Program
low-to-moderate income
multi-family owner-occupied building
multi-family renter-occupied building
National Center for Education Statistics
National Historic Geographic Information System
National Housing Preservation Database
National Land Cover Database
National Renewable Energy Laboratory
National Solar Radiation Database
OpenStreetMap
public use microdata sample
photovoltaic
Residential Energy Consumption Survey
Regional Energy Deployment System
Rooftop Energy Potential of Low Income Communities in America
Renewable Energy Potential Model
single-family owner-occupied building
single-family renter-occupied building
typical meteorological year2
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

TMY3
TWh

Typical Meteorological Year Version 3
terawatt hour3This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Executive Summary
Adoption of rooftop solar in the United States primarily has been concentrated in higher-income
households (Moezzi et al. 2017; Vaishnav et al. 2017). As technology costs decline and markets
expand, however, focus is shifting to increasing solar access in underserved market segments—
particularly to low-to-moderate income (LMI) households, or those earning 80% or less of thearea median income (AMI). A key policy goal is to expand solar access more equitably to ensure
the benefits of solar, including reduced energy burden, increased resilience, and hedge againstelectricity rate changes are available to all ratepayers. To achieve this goal, a deeper
understanding of the potential LMI market is needed. Although LMI households represent about
43% of the U.S. population, it is unknown what proportion live in buildings suitable for PV, how
this potential is distributed among the buildings they live in, or what fraction of their electricity
needs could be met with rooftop solar.
This report serves to expand upon previous NREL research investigating the technical potentialof rooftop solar in the United States, aiming to improve the understanding in the residential
sector, particularly for low-to-moderate income households. Technical potential is a metric thatquantifies the maximum generation available from a technology for a given region and does notconsider the economic or market viability. A unique contribution of this work is to estimate
rooftop solar technical potential of residential buildings per U.S. Census Tract by income,building type, and tenure. The underlying data—as documented in Gagnon et al. 2016—are aseries of light detection and ranging (LiDAR)–based scans of the building stock in 128 metro
regions. Using LiDAR scans, as opposed to aerial imagery, allows researchers to infer thebuilding footprint and the unshaded roof area, azimuth, and tilt for each distinct roof plane,
although roof age or other structural concerns are not considered. This data then is intersectedwith U.S. Census Bureau socio-demographic and building stock data at the tract-level to better
understand how rooftop solar technical potential is allocated among different building types.
This research also uses statistical techniques to estimate rooftop potential in areas not covered by
the LiDAR scans. Collectively, these data and methods are used to address three researchquestions.
1. What is the quantity and spatial distribution of rooftop technical potential,
stratified by income, building type, and tenure? At a gross level, these data enableimproved estimates of the possible opportunity for deployment, as well as the magnitude
of various policy interventions.
2. Among low-to-moderate-income households, what is the feasibility of achieving
parity in solar access across income groups? Compared to high-income households,
LMI households disproportionately live in rental-occupied, multi-family buildings.
Because of principal-agent issues in solar adoption, there are significant economic andregulatory barriers inhibiting the installation of solar equipment on buildings occupied by
LMI households. This study investigates the feasibility of offsetting at least 33% of LMI
household electrical consumption with solar energy and, where infeasible, highlightconsideration of novel deployment models—such as community solar, virtual net
metering, and other shared solar models.
3. Finally, low- and moderate-income households interact with a vast web of nonprofit
entities (e.g., places of worship and public schools). What is the quantity of technical4This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

potential for these classes of buildings, and to what extent might these buildings“oversize” systems on their roofs to export excess generation to LMI households?
Increasing access to solar for such third-parties could reduce these entities’ operationalcosts and indirectly provide roof space to households without access.

National Residential Rooftop PV Technical Potential
Over all tracts and all residential buildings in the United States, the estimated residential rooftop
solar technical potential is nearly 1,000 terawatt-hour (TWh) of generation, or about 75% ofresidential consumption. Significant potential is found in every income group (Table ES-1), withthe greatest overall potential in the non-LMI (>80% AMI) income group, and with 416 TWh for
low-to-moderate (0% to 80% AMI) households over 25.5 million solar-suitable buildings. The
LMI potential is approximately 42% of the total U.S. residential potential, a considerable portionof the overall rooftop opportunity (Figure ES-1).
Table ES-1. National Residential PV Rooftop Technical Potential by Income GroupHouseholds(millions)

Suitable
Buildings(millions)

Suitable
ModuleArea(millions ofm2)

Capacity
Potential(GWDC)

AnnualGeneration
Potential(TWh/year)

Very Low(0%–30% AMI)

19.5

9.4

794.4

127.1

160.8

Low
(30%–50% AMI)

11.5

5.7

472.8

75.6

95.3

Moderate(50%–80% AMI)

18.8

10.4

792.0

126.7

159.8

Middle(80%–120%
AMI)

21.1

12.3

900.4

144.1

180.8

High
(> 120% AMI)

46.0

29.4

2,003.3

320.5

403.1

All LMI Buildings

49.8

25.5

2,059.2

329.4

415.9

All Residential Buildings

116.9

67.2

4,962.9

794.0

999.8

Income GroupLMI

NonLMI

A majority of the overall residential potential (683 TWh, 68.4%) is situated on single-family
buildings, as compared to multi-family dwellings (316 TWh, 31.6%) and single-family potential
exceeded multi-family potential for each income group. Similar ratios are seen for owneroccupied and renter-occupied buildings as there is a strong correlation between multi-family
occupancy and rental status. For LMI households specifically, the largest modality of potential isfor single-family owner-occupied buildings (176.8 TWh), followed by multi-family renteroccupied buildings (140.1 TWh). Though deployment of rooftop solar historically has been
concentrated on single-family owner-occupied buildings, nearly 60% of potential for LMI
buildings exists on renter-occupied and multi-family buildings.

5This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure ES-1. County LMI rooftop technical potential as percent of total residential potential

Feasibility of Parity in Adoption Rates
The U.S. Department of Energy (DOE) Solar Energy Technology Office recently announced
updated cost targets for solar energy, which could result in 971 GW of solar capacity, providing
33% of electrical generation by 2050 (Cole et al. 2017). Inspired by this goal we investigate thefeasibility of rooftop solar offsetting at least 33% of low- and moderate-income household
electrical consumption in each U.S. county. Note that the data does not enable assessment of thefeasible offset fraction at the household level, and instead should be interpreted in aggregate overthe county. The analysis also only considers consumption offsets over an annual basis and does
not consider hourly mismatches in generation and consumption—which could be substantial
without energy storage or other load-shifting methods.
Offsetting 33% of LMI household electrical consumption (“offset target”) with rooftop solar istechnically feasible on a national scale when only considering households in single-family
owner-occupied (SFOO) buildings, although to do so requires buildout on essentially all SFOO
buildings—an impractical and unforgiving market challenge. In contrast, on a technical basis,
there is more than sufficient roof space to meet the 33% offset target when including singlefamily rental-occupied (SFRO), multi-family owner-occupied (MFOO), and multi-family renteroccupied (MFRO) buildings (Figure ES-2). Specifically, the average fraction of generation
potential to consumption nationally is 85% for the Very Low, 92% for Low, 80% for Moderate,
and 70% for Non-LMI income groups. The lower per-capita levels of consumption for lowincome households makes offset targets more feasible for this group than for non-LMIhouseholds.

6This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure ES-2. Technical feasibility of matching residential electrical consumption withrooftop solar (shown by income group)

Not all counties can offset 33% of their LMI consumption with in-county rooftop solar. Whenconsidering only single-family owner-occupied buildings, 40% of U.S. counties have insufficient
rooftop solar potential to offset 33% of LMI electrical consumption (Figure ES-3 left), thoughwhen including the remaining residential building stock this figure decreases to 1% (Figure ES-3
right). In the latter condition, shortfalls are most significant for mobile homes (38 TWh)—whoseroofs are typically not suitable for solar, followed by single-family (6 TWh) and multi-family(2 TWh) buildings. Spatial trends in the potential for solar to offset LMI consumption moststrongly reflected regional variation in per-capita electricity consumed, primarily due to which
fuels are used for building heating and cooling loads. Other trends include solar irradiance,building-stock composition, and the prevalence of solar-suitable rooftops.

Figure ES-3. Percent of LMI electrical consumption that can be offset by rooftop solar
generation—single-family owner-occupied LMI buildings only (left), all LMI buildings (right)

From a technical basis alone, there is sufficient rooftop space for at least 33% of LMI
consumption to be met with rooftop solar generation. Reaching this potential, however, requires
7This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

the deployment of models other than those commonly found today, particularly, models that canaddress coordination issues inherent to rental-occupied and multi-family buildings. Such models
must ensure that rental-property owners are incentivized to install solar on their buildings—for
example, by bundling utility expenses with rent payments as a means of passing solar costs andsavings through to the renter. These models also would need to address the diverging
requirements and energy burdens of owners and tenants. For example, California recently
developed an incentive program dedicated to affordable multi-family housing, with requirementsthat at least half of energy generated onsite be used to serve tenants loads (CPUC 2017b). In
Colorado, the Denver Housing Authority’s 2 MW low-income community solar garden has
demonstrated a scalable model for offsite generation through utility partnerships (DOE 2017).
Virtual net metering also can be effective in enabling building owners to provide surplus
generation directly to their occupants (Feldman et al. 2015).

Technical Potential for Buildings that Serve LMI Populations
Low- and moderate-income households interact with a vast web of non-profit entities, and it isplausible that these buildings could “oversize” photovoltaic (PV) systems on their buildings toshare solar generation with their communities. We estimate the generation potential within thecities of Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C., for fivebuilding types: public sites, public housing, K–12 public schools, homeless shelters, and placesof worship. These cities were selected for congruence with a forthcoming companion analysis of
the technical, market, regulatory, and social factors affecting LMI solar markets in those threecities. Based on the building’s size and average electrical consumption per square foot forcomparable buildings, each building’s electrical consumption was estimated, and thus thefeasibility of oversizing systems to export excess generation to nearby LMI households. Basedon these assumption, buildings in Chicago, San Bernardino/Riverside, and Washington, D.C.
could feasibly oversize PV systems to provide 2.1%, 8.7%, and 1.3% of LMI consumption,
respectively. None of the estimates include on-site ground-mounted systems.
Schools, followed by places of worship had the greatest opportunity to export generation to thecommunity of the five building classes considered. Schools typically have large flat roofs anddecrease consumption in the summer, when solar irradiance is highest. Places of worship arefavorable because they have low levels of electricity consumption year-round and moderatelyfavorable roof characteristics. Unfortunately, public housing, public sites, and homeless shelters
likely have insufficient rooftop space to offset 100% of their own on-site consumption. These
results indicate that there is a modest, though not overwhelming, opportunity for buildings thatserve LMI populations to oversize a PV system—and that projects are best evaluated on a caseby-case basis.

Conclusions
This report presents a first-of-its-kind assessment of the technical potential of rooftop solar forlow- and moderate-income households, as well as providing insight on the distribution of solar
potential by tenure, income, and other building characteristics. The findings show that asubstantial fraction of the national rooftop solar potential is located on LMI buildings and, for all
incomes, a substantial fraction is located on multi-family and renter-occupied buildings. The
research also demonstrates that reaching substantial LMI deployment penetration requires
deployment on multi-family and renter-occupied buildings. Traditional deployment models have

8This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

insufficiently enabled access to solar for these income groups and building types and, without
additional innovation either in regulatory, market, or policy arenas, a large fraction of the U.S.
potential is unlikely to be met. Potential electric bill savings from rooftop solar would have thegreatest material impact on the lives of low-income households as compared to their highincome counterparts and could help mitigate the energy burden faced by these households.
This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits,
and project developers to make informed decisions that are best for their own communities. To
this effect, data used in this report are provided freely via NREL’s website in two formats. One
format—an interactive web application using NREL’s OpenCarto platform—was developed toenable user to browse, visualize, and export results (https://maps.nrel.gov/solar-for-all). The
other format is the Rooftop Energy Potential of Low Income Communities in America(REPLICA) data set (https:/data.nrel.gov/submissions/81) which contains the technical potential
data used in this report, accompanied by several additional techno-economic variables (e.g.
electricity expenditures ($/month), demographics, utility electric rates) which are available fordownload as flat files.

9This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table of Contents
1. Introduction ............................................................................................................................................. 1
2. Methods.................................................................................................................................................... 4

2.1. Using LiDAR Data to Estimate PV Rooftop Technical Potential ................................................. 4
2.2. Simulating PV Productivity Using reV and PVWatts ................................................................... 6
2.3. Estimating National LMI PV Rooftop Technical Potential by Tract ............................................ 7
2.3.1. Estimating Number of LMI Households by Building Type and by Tenure ..................... 7
2.3.2. Estimating LMI Technical Potential for LiDAR Covered Tracts .................................... 9
2.3.3. Imputing LMI Technical Potential for Tracts Outside of LiDAR Coverage ................. 11
2.4. Model Uncertainty and Caution in Interpreting Results.............................................................. 122.4.1 Uncertainty of solar potential for LiDAR-covered and non-covered areas.................... 122.4.2 Uncertainty of distribution of solar potential by income-tenure-types........................... 13
3. Results ................................................................................................................................................... 14

3.1. National Residential Rooftop PV Technical Potential ................................................................ 14
3.1.1. Spatial Trends in LMI Technical Potential .................................................................... 15
3.1.2. Technical Potential by Tenure, Building Type, and Income .......................................... 18
3.2. Feasibility of Parity in Solar Adoption Rates .............................................................................. 21
3.2.1. Additional Factors Impacting Building Technical Suitability........................................ 29
3.3. Technical Potential for Select Buildings that Serve LMI Households ........................................ 30
4. Conclusion ............................................................................................................................................. 33

4.1. Data for Public Use ..................................................................................................................... 33
4.2. Future Work ................................................................................................................................ 34
References ................................................................................................................................................. 35
Appendix A. Allocation of Demographic Attributes .............................................................................. 39

A.1. Calculate the Number of Households Belonging to Each $1,000 Income Group, at the Tract
Level 39
A.2. Disaggregate from County Data to Estimate the Number of Households, per $1,000 Income
Group, per Tenure, at the Tract Level ......................................................................................... 40
A.3. Disaggregate from County Data to Estimate the Number of Households, per $1,000 Income
Group, per Tenure, per Dwelling Type, per Household Size ...................................................... 40
A.4. Aggregate Tract Estimates by AMI Income Group .................................................................... 40
Appendix B. Validation of Monte Carlo Simulations for Building Sampling ...................................... 41
Appendix C. Predictive Modeling Framework for Imputing Solar Suitability ..................................... 46

C.1. Household-to-Building Model .................................................................................................... 46
C.1.1. Household-to-Building-Count Ratios ............................................................................... 46
C.1.2. Building-Type-to-Building-Size Ratios............................................................................ 47
C.2. Small Building Suitability Model ................................................................................................. 47
C.3. Rooftop Plane Area Model .......................................................................................................... 48
C.4. Rooftop Tilt and Azimuth Model ................................................................................................ 50
Appendix D. Quantifying the Technical Potential and On-Site Consumption of LMI-Serving
Buildings in Three Case Study Cities ............................................................................................... 52

D.1. Quantifying the Technical Potential of Third-Party Buildings ................................................... 52
K–12 Public Schools ................................................................................................................... 54
Homeless Shelters ....................................................................................................................... 54
Places of Worship........................................................................................................................ 54
Public Housing ............................................................................................................................ 54
Public Sites .................................................................................................................................. 55
D.2. Estimating the On-Site Consumption of Third-Party Buildings ................................................. 55

10This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Figures
Figure 1. LiDAR data coverage .................................................................................................................... 5
Figure 2. County residential technical potential (GWh) ............................................................................. 16
Figure 3. County LMI rooftop technical potential as percent of total residential potential ........................ 17
Figure 4. Percentage of tract-level rooftop solar potential (MW) on LMI buildings.................................. 18
Figure 5. Generation potential by income group and building type............................................................ 19
Figure 6. Generation potential by income group and building tenure ........................................................ 19
Figure 7. Generation potential (TWh) by building type and tenure ............................................................ 20
Figure 8. Comparison of fraction of consumption met by generation potential for single-family owneroccupied buildings alone, by income group ........................................................................... 25
Figure 9. Technical feasibility of matching residential electrical consumption with rooftop solar, byincome group.......................................................................................................................... 26
Figure 10. Percent of LMI electrical consumption that can be offset by rooftop solar generation(county)—all LMI buildings .................................................................................................. 27
Figure 11. Percent of LMI electrical consumption that can be offset by rooftop solar generation(county)—single-family owner-occupied LMI buildings only .............................................. 28

List of Tables
Table ES-1. National Residential PV Rooftop Technical Potential by Income Group................................. 5
Table 1. Requirements for Solar Suitable Surfaces ...................................................................................... 6
Table 2. Assumptions for PV Performance Simulations .............................................................................. 7
Table 3. 2015 Five-Year American Community Survey Published Tables Used ......................................... 8
Table 4. Building Type to Building Size Schema ....................................................................................... 10
Table 5. National Residential PV Rooftop Technical Potential by Income Group..................................... 15
Table 6. National residential PV rooftop technical potential by income group .......................................... 21
Table 7. Annual Residential Electricity Consumption by Income, Building Tenure, and Type................. 23
Table 8. Estimates of National Shortfall in Rooftop Solar Generation Needed to Nationally
Offset 17% and 33% of LMI Consumption ......................................................................................... 26
Table 9. Annual Generation Potential for Select Third-Party Buildings in Chicago, Washington
D.C., and San Bernardino/Riverside (GWh) ........................................................................................ 31
Table 10. Net Excess Rooftop Potential Accounting for Building Self-Consumption by Building
Class and City ....................................................................................................................................... 32
Table B-1. Assumptions for PV Performance Simulations Summary Statistics for City-Level
Technical Potential ............................................................................................................................... 42
Table C-1. National Mean Household-to-Building Ratio ........................................................................... 46
Table C-2. National Mean Building Type to Building Size Ratio for Census Tracts ................................. 47
Table C-3. ANOVA of Small Building Suitability OLS Model ................................................................. 48
Table C-4. National Distributions of Building Plane Count Probabilities by Building Size Class ............ 49
Table C-5. Fitted Areas for Each ith Plane, per Building Size Class .......................................................... 50
Table C-6. National Average Tilt/Azimuth Distribution for Building Planes, by Building Size Class ...... 51
Table D-1. Core GIS Data Sources for LMI-serving Buildings in Case Study Cities ................................ 53
Table D-2. Parcel Data Used for Appropriating Buildings to Public Housing Properties .......................... 55
Table D-3. Representative Building Types Used for Third-Party Building Energy Consumption
Estimates .............................................................................................................................................. 56

11This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1. Introduction
Residential solar offers a compelling opportunity for reducing the energy burdens faced by lowincome households, whose utility bills comprise a larger fraction of expenses than for highincome households. Moreover, because residential solar has disproportionately been adopted byhigh-income households (Moezzi et al. 2017; Vaishnav et al. 2017), often benefiting frompublic-funded incentives, it is important to ensure that all utility ratepayers have access to solar
benefits including reduced energy burden, increased resilience, and as a hedge against electricity
rate changes.
This report seeks to improve understanding of the technical potential of rooftop solar in theresidential sector, particularly for low- to moderate- (LMI) income households. Technicalpotential is a metric that quantifies the maximum generation available from a technology for agiven region and does not consider the economic or market viability. This report expands uponprevious NREL research investigating the rooftop solar technical potential using light detecting
and ranging (LiDAR) scans of individual rooftops in 128 metro regions (Gagnon et al. 2018;
Margolis et al. 2017; Gagnon et al. 2016; Phillips and Melius 2016). Using LiDAR scans, as
opposed to aerial imagery, allows us to infer the building footprint and the unshaded roof area,
azimuth, and tilt for each distinct roof plane. A unique contribution of this work is to estimate
rooftop solar technical potential of residential buildings per U.S. Census tract by income,building type, and tenure. To do so, the LiDAR data set is intersected with U.S. Census Bureau
socio-demographic and building stock data at the tract-level to better understand how rooftop
solar technical potential is allocated among different building types. Statistical techniques also
are used to estimate rooftop potential in areas not covered by the LiDAR scans. Collectively,
these data and methods enable the study to address three research questions:
1. What is the quantity and spatial distribution of rooftop technical potential,
stratified by income, building type, and tenure? At a gross level, these data allow
improved estimates of the possible opportunity for deployment, as well as the magnitude
of various policy interventions.
2. Among low and moderate-income households, what is the feasibility of achieving
parity in solar access across income groups? Compared to high-income households,
LMI households disproportionately live in renter-occupied, multi-family buildings.
Because of principal-agent issues in solar adoption, there are significant economic andregulatory barriers inhibiting LMI households from benefiting from the installation ofsolar on buildings they occupy. Recent U.S. Department of Energy (DOE) solar costgoals could result in solar penetration rates of 17% by 2030 and 33% by 2050 as afraction of national electricity consumption. This report investigates the feasibility ofreaching at least 33% solar penetration for LMI households and, where infeasible,
highlights consideration of novel deployment models, such as community solar, virtual
net metering, and other shared solar models.
3. Finally, low- and moderate-income households interact with a vast web of nonprofit
entities (e.g., churches, schools, public sites, homeless shelters, subsidized housing).
What is the quantity of technical potential for these classes of buildings, and to what
extent might these buildings “oversize” systems on their roofs to export generation
to LMI households? Increasing access to solar for such third-parties could either
1This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

increase their operational efficiency by reducing operational costs, or indirectly provide
roof space to households without access.
On a technical basis, the potential for rooftop solar to serve a large fraction of the nation’s energy
needs is well-recognized (Gagnon et al. 2016; Lopez et al. 2012; Denholm and Margolis 2008;
Paidipati et al. 2008; Chaudhari et al. 2004). Rooftops provide a large and sparsely used space toinstall panels; onsite solar generation can reduce the building’s net demand for grid-sourcedenergy and can reduce losses associated with the distribution and transmission of electricity.
Recent analysis has estimated that buildout of all suitable roof space could serve approximately
38.6% of U.S. load (Gagnon et al. 2016). This potential is disproportionately located within theresidential sector, with small buildings (<5,000 ft2) contributing nearly 65% of the nation’s
potential (Gagnon et al. 2016). Previous work, however, has not explicitly considered theintersection of rooftop solar potential with income, building characteristics (e.g., rental versus
owner-occupied), and other demographic factors. Though building tenure is not relevant on atechnical basis alone, it is very significant from a market perspective.
Adoption of distributed solar in the U.S. residential sector to date primarily has been
concentrated among high-income households (Moezzi et al. 2017; Vaishnav et al. 2017).
Unlocking technology access and demand from all households could substantially increase
market potential—in 2015 43% of all households could be considered to have low to moderate
income (<80% area median income(AMI)) and 18% to be middle income (80% to 120% AMI)
(U.S. Census 2015). Further, it is not equitable for solar adoption to occur only among highincome households. Recent studies have explored the transfer of wealth implied by technology
incentives and net metering policies, finding that majority of public subsidies have been passed
through to high-income households (Vaishnav et al. 2017; Borenstein 2017). Without recourse,rooftop solar risks a reputational backlash—in time, it could be perceived as solely the purview
of the wealthy, and its deployment not seen as a public benefit.
Ensuring access to affordable energy for all households is a policy mandate for federal, state, andlocal governments. Costs of energy for low-income households can comprise a substantial
fraction of household income and, as a result, many programs exist to ensure its affordability. A
notable example is the Low-Income Home Energy Assistance Program (LIHEAP) run by the
U.S. Department of Health and Human Services. The program provides financial assistance forhome energy bills, energy crises, and weatherization and energy-related minor home repairs(HHS 2017). The U.S. Department of Energy Weatherization Assistance Program also provides
assistance for LMI households by increasing the energy efficiency of their homes (DOE 2018).
A state-level model is the California Alternate Rates for Energy (CPUC 2017a) program, which
gives low-income customers discounts on their electricity and natural gas rates.
As the cost of solar energy continues to decline, many have begun to question what role solar canplay in increasing energy affordability or as an efficient alternative or supplemental use of publicfunds for energy-assistance programs. For instance, use of funds to directly or indirectly
incentivize solar deployment might be more cost-efficient than rate subsidization, because it
creates a durable asset for low-income households and in the long term could lead to greater
program effectiveness. The exact cost-benefit of diverting rate-assistance funds depends on
program details and is beyond the scope of this study.

2This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Incentivizing solar deployment in LMI communities requires a reimagination of traditional
deployment models. These new models should address coordination issues inherent to rentaloccupied and multi-family buildings as well as LMI financing and affordability barriers (Cookand Bird 2018). Such models also should ensure that rental-property owners are incentivized to
install solar on their buildings, for example, by bundling utility expenses with rent payments as ameans of passing savings through to the renter. Alternatively, various shared solar models(Feldman et al. 2015), including virtual net metering, could be effective in allowing building
owners to sell rooftop generation directly to the building occupants. Similar issues exist for
multi-family buildings, where shared solar or community solar models could help to address
tenant-owner coordination issues.
This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits,
and project developers to make informed decisions that are best for their own communities; forexample, to assess the potential for rooftop solar in their jurisdictions and perform policy costbenefit analyses. To that end, this report is accompanied by two supporting data products. One is
an interactive web application that uses NREL’s OpenCarto platform; it was developed to enable
users to browse, visualize, and export results (https://maps.nrel.gov/solar-for-all). Another is thetechnical potential data used in this report—accompanied by several additional techno-economic
variables and aggregated at the tract-level—available for download at(https://data.nrel.gov/submissions/81). The Rooftop Energy Potential of Low Income
Communities in America (REPLICA) data set includes measures of electricity expenditures($/month), demographics, utility electric rates, state-level photovoltaic (PV) incentives, measuresof environmental quality, and locations of public housing, compiled from a variety of sources
and tagged to each Census Tract.
Many opportunities exist to improve the methods used in this report, but there are threeimportant restrictions. One is that the geographic span of LiDAR aerial rooftop imagery is
restricted to 128 metro regions, spanning approximately 40% of the U.S. population. A statisticalmodel was developed and validated to estimate potential for unsampled areas, particularly forrural areas. Section 2.2.3 discusses efforts to train and validate this model. Future work could
improve the geographic coverage of LiDAR scans in these areas. The second, is that the LiDAR
data do not enable the direct observation of the true income, tenure, number of housing units, orincome of the building occupants. Instead, these attributes are assigned from tract-level crosstabulation tables from the 2011–2015 U.S. Census American Community Survey using statisticaltechniques, including sampling from probability distributions. The final tract-level estimates are
based on the median value of a 100-run Monte Carlo process (see Appendix B). This sampling
process could be greatly improved with access to a national zoning or parcel data set, which
would contain a richer set of property-level attributes. Finally, this analysis solely considers thetechnical potential of rooftop solar—or the total solar resource that could be captured given
physically-available area and technology performance. Notably, this analysis does not consider
the economic or market feasibly of solar installations, nor the feasibly of on-site use of the solar
generation.

3
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2. Methods
This section describes the methods used to estimate PV rooftop technical potential by Census
tract, stratified by income, building type, and tenure. It includes methods to process LiDAR
rooftop scans in the 128 metro regions with coverage (Section 2.1) and estimate their generation
productivity (Section 2.1.2). Next, it discusses the intersection of tract-level technical potential
and demographic cross-tabulations (Section 2.2.1 and Section 2.2.2). Finally, a statistical modelof rooftop potential for the remaining regions of the United States (Section 2.2) is estimated bytraining it on sampled areas (i.e., those with LiDAR rooftop observations). Additional details on
methods are described in the appendices: Allocation of Demographic Attributes (Appendix A);
Validation of Monte Carlo Simulations for Building Sampling (Appendix B); Predictive
Modeling Framework for Imputing Solar Suitability (C1–C4).

2.1. Using LiDAR Data to Estimate PV Rooftop Technical Potential
This analysis builds upon previous work pioneered by NREL (Gagnon et al. 2016) using LiDAR
data to model rooftop suitability for solar photovoltaics. Light Detection and Ranging (LiDAR)
is a remote-sensing method that uses pulsed laser beams to measure distances to the ground. The
LiDAR sensing algorithms can be used to infer (1) the presence of individual buildings and theirfootprint within a city, and (2) the area, tilt, azimuth, and shading of each distinct geometric roof
plane on a building’s roof. Based on these roof characteristics, the NREL PVWatts (NREL 2017)
tool is used to estimate the technical performance for the individual building and thus thecollective tract-level building stock.
This work relies on LiDAR data sets provided by the U.S. Department of Homeland Security(DHS) Homeland Security Infrastructure program for 2006–2014. This data set consists of firstreturn rasters of 1 m2 resolution and building footprint vectors for 128 metropolitan areas acrossthe nation. Figure 1 shows the metro regions with LiDAR coverage, where the blue polygons are
the areas with observed data. In total, this data includes 26.9 million buildings, or about 23% of
U.S. buildings (EIA 2013) and covers an area that representing about 40% of the U.S.
population.

4
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 1. LiDAR data coverage

Using these LiDAR data, Gagnon et al. (2016) developed a geospatial model to identify rooftop
planes suitable for rooftop-mounted solar PV given the roof’s orientation (tilt and azimuth) andshading characteristics. To account for potential shading from adjacent buildings, trees, or otherobstacles, NREL researchers generated hourly hillshades that identify roof square-pixel areas
with sufficient sunlight illumination. To identify developable surfaces for PV installment, a zonal
mean neighborhood function was used to identify and remove data noise and complex features
on roofs (e.g., peaks, edges, chimneys, steeples, facades). Finally, roofs were filtered for rooftop
developable surfaces that met basic PV suitability requirements, such as being south-facing andhaving a minimum area of 10 m2 (Table 1). The end result is a database of rooftop plane-level
data with detailed attribution regarding the slope, tilt, azimuth, installed capacity, and annual
generation potential (kWh/year) (discussed in more detail in Section 3.1.2) of all suitablesurfaces on all rooftops in the 128 LiDAR–covered metropolitan areas. This data set was animportant milestone in the distributed PV field and has paved the way for many subsequent
analyses, including the work presented in this report.

5This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 1. Requirements for Solar Suitable Surfaces
Requirement

Description

Shading

Seasonal requirements 1: March requires 60% illumination, June requires 70%
illumination, September requires 60% illumination, and December requires 50%
illumination.

AzimuthEast, Southeast, South, Southwest, or West-facing

Tilt

Average surface tilt <= 60 degree.

Minimum Area 2>= 10 m2

Source: Gagnon et al. (2016).

2.2. Simulating PV Productivity Using reV and PVWatts
Simulations of solar generation were conducted at the tract-level and for 21 tilt-azimuth
combinations using the NREL reV tool. 3 The Renewable Energy Potential (reV) model’s
generation module is a complex wrapper that enables distributed generator performancemodeling using NREL’s PVWatts model with large renewable resource databases in a parallel
computing environment. To simulate PV productivity, reV uses multiple historic solar irradiancetime series data from the National Solar Radiation Database (NSRDB), producing generation andcapacity factor profiles for a typical meteorological year (TMY). Generation profiles based on
TMY data capture hourly variability of an average year and are estimates of the long-term
performance of a solar system, though they do not capture significant annual variation in
generation due to weather/storm events.
The solar resource and meteorological data used by reV/PVWatts for this analysis are from the
National Solar Radiation Database TMY3 data (Wilcox and Marion 2008). These data include
hourly representative profiles for 1,001 stations throughout the United States. Each Census Tract
is assigned the TMY3 irradiance profile for the weather station closest to the tract’s centroid. We
also relied on the set of standard technical assumptions used by Gagnon et al. (2016)
representing the 2015 average performance of PV systems (Table 2).

1

Seasonal shading requirements, as defined by Gagnon et al. (2016), are used to identify suitable rooftops withadequate percent sunlight illumination throughout the year. Refer to Gagnon et al. (2016) for further details.
2
Based on current panel power density, 10 m2 provides sufficient area to install a 1.5 kW system. This minimum
area threshold was chosen to represent a conservative lower-end estimate of viable PV system sizes based on current
PV performance and historic patterns in reported PV sizing.
3
Documentation for reV did not exist at the time of writing, but additional information is available athttps://www.nrel.gov/gis/modeling.html.

6This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 2. Assumptions for PV Performance Simulations
PV System Characteristics

Value for Flat Roofs

Value for Tilted Roofs

Tilt

15 degrees

Midpoint of tilt class

Ratio of module area to roof area 4

0.70

0.98

Azimuth180 degrees (south facing)

Midpoint of azimuth class

Module power density

160 W/m2

Total system losses

14.08%

Inverter efficiency

96%

DC-to-AC ratio 5

1.2

Source: Gagnon et al. (2016).

2.3. Estimating National LMI PV Rooftop Technical Potential by Tract
From the LiDAR PV rooftop data set described in Section 2.2, we extend the data set to estimate
tract-level solar technical potential by building income, tenure, and building size. This consists ofthree broad steps: (1) Intersecting individual building technical potential estimates withdemographic factors from the Census Bureau 2011–2015 American Community Survey; (2)Aggregating the building-level estimates to the tract, county, and state level and conducting
Monte Carlo runs to determine the median estimate; and (3) Training a statistical model on tracts
with LiDAR coverage and imputing technical potential for tracts outside of LiDAR coverage.
2.3.1. Estimating Number of LMI Households by Building Type and by Tenure
For the 128 metro regions with LiDAR-derived PV rooftop technical potential, we overlaid
demographic data to estimate the total technical potential for residential buildings, per income group,
per building type, and per tenure. This work relies on demographic data from the American
Community Survey’s (ACS) 2011–2015 5-year estimates; see Table 3 for a detailed account ontables used in this analysis. The ACS publishes these data as smaller crosstabs, with much of thedetail available only at the larger (i.e., county) geographic levels.6 To account for this, we use
random weighted sampling and proportional allocation methods to disaggregate demographic data
into a tract-level cross-tabulation of household counts by AMI income group, per building type andtenure. Additional details on the demographic cross-tabulation process are provided in Appendix A.

4

For flat roofs, the ratio of module area to roof area was assumed to 0.7 to reflect the row spacing necessary to incur
only approximately 2.5% losses from self-shading for south-facing modules at a 15-degree tilt. For tilted roofs, thevalue was assumed to be 0.98 to reflect the 1.27 cm spacing between each module for racking clamps.
5
A system’s direct current to alternating current (DC-to-AC) ratio is the ratio of the nameplate capacity of the PV
modules to the AC-rated capacity of the inverters.
6
Though much of the data is available at the county level, Census Tracts were chosen as the unit of analysis in thisreport for a few reasons. First, Census Tracts are more homogenous than the larger county and therefore are better
areas for delineating between income groupings within a city or region. Second, the underlying LiDAR data isavailable at the sub-tract level at a 1-meter resolution. Finally, Census Tracts provide a better unit of analysis from a
policy and planning perspective, which is the target audience of this report and data.

7
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 3. 2015 Five-Year American Community Survey Published Tables Used
Table

Source Code

Universe

Geography

Household Income in the Past 12 Months(in 2015 Inflation-Adjusted Dollars)

B19001

Households

Tract

Tenure by Household Income in the Past 12
Months (in 2015 Inflation-Adjusted Dollars)

B25118

Occupied housing units

CountyTenure by Units in Structure

B25032

Occupied housing units

CountyTenure by Household Size by Units in
Structure

B25124

Occupied housing units

CountySource: U.S. Census 2015a, 2015b, 2015c. 7This work defines LMI based on the Department of Housing and Urban Development’s (HUD)
2016 Area Median Income (AMI) income limits (HUD 2016). These AMI income limits areused to determine the eligibility of applicants for federal assistance programs. They are based off
the median income for Fair Market Rent (FMR) areas (i.e., metropolitan areas, parts of somemetropolitan areas, and non-metropolitan counties) and are adjusted based on the family size 8(HUD PD&R). Using these AMI income limits and the generated ACS crosstab described above,
we categorize households into the following low- to moderate-income groups, as defined by theCommunity Development Block Grant (CBDG), 9 based on household income and the number ofpeople in the household:•

Very Low Income: 0% to 30% of AMI 10•

Low Income: 30% to 50% of AMI•

Moderate Income: 50% to 80% of AMI•

Middle Income: 80% to 120% of AMI•

High Income: >120% of AMI

7

Data extracted using NHGIS (Manson et al. 2017).
The HUD baseline numbers for each income limit are based off the 4-person family size. For households withlarger or smaller sizes, percentage adjustments are made to the income break limits based off the number of people
in the household (HUD PD&R 2016).
9
The income categories used in this report are derived from the Community Development Block Grant (CDBG).
Though there are many ways in which low- and moderate-income thresholds could be defined, we use the CDBG
definition because (1) it includes a class for moderate income, whereas the standard Section-8 definition does not,and (2) it estimates income based on the local geography and the relative cost of living in a particular location. It is
important to note that LMI can best be described as a gradient and that delineating between groups does not capture
the whole story. Indeed, many assistance programs define “moderate” as a range that extends into what we classify
as middle income (up to 120% of AMI) in this report.
10
The Very-Low Income break limit is based on the greater of the two: (1) 30% AMI or (2) the Federal Poverty
Level (FPL), defined by the Department of Health and Human Services (HHS), capped at the low-income limit(50% of AMI) (HUD PD&R 2016). As a result, this leads to fewer households reported in the low-income bin ascompared to the very-low income bin. For example, in tracts where the 30% AMI is less than the FPL, the FPL(capped at the low income 50% AMI level) is used as the break limit.
8

8This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

To allocate from ACS income bins to the above income categories we discretize the standard
ACS income bins into $1,000 increments and apply the HUD income breakpoints. This allows us
to align different ACS crosstabs with different income bins and classify households into the five
AMI income groups described above using the AMI breakpoints associated with each household
size class. Using a random weighted sampling and proportional allocation approach, wedisaggregate county-level tenure and building-type estimates by income group. Further details onthese data manipulations and key sampling assumptions are outlined in Appendix A. The result
is a data set with total number of households per income group, per building type, and per tenure
for all 72,760 tracts in the United States. The next section describes how this data set is joinedwith solar-generation estimates.
2.3.2. Estimating LMI Technical Potential for LiDAR Covered Tracts
We developed a model to estimate the number of occupied residential buildings and gross rooftop
technical potential for tracts with LiDAR coverage. 11 In total, LiDAR-covered tracts compriseapproximately 32% of the total U.S. Census Tracts. Starting with the demographic crosstab
described above, we rely on a series of methods tailored toward translating the counts ofhouseholds per building type (e.g., single-family detached, 2–4 units, 5+ units) into estimates ofbuilding counts by building size class (i.e., small, medium, large). This crosstab ultimately is used
to assign building attributes to the individual building IDs from the LiDAR database; LiDAR dataalone cannot identify characteristics of building occupants for individual buildings (e.g., residential
or not, multi-family or not, low-income or not). To address this challenge, we rely on a few coreassumptions and leverage a variety of data to improve the precision of the sampling and allocation
process. Note that parcel data—such as property tax assessment—would be a more accuratealternative although no such public data set exists with detailed attribution at a national level.
First, we define a schema of building type to building size (Table 4) to map from building types(e.g., 2- to 4-unit buildings, single family) as reported in the American Community Survey tobuilding sizes in the LiDAR data (e.g., small buildings; 0 to 5,000 ft2). This schema was derived
from the 2009 EIA Residential Energy Consumption Survey (RECS) microdata (EIA 2013),
which reports frequencies of total floor space, number of stories, and building types. To estimate
the building footprint area, the amount of floor space is divided by the number of building
stories. From these calculations, we identified that 99.8% of residential buildings in the UnitedStates belong to either the small (< 5,000 ft2 footprint) or medium (5,000 to 25,000 ft2 footprint)
building classes, 99% of single family buildings are small, 87% of multi-family buildings are
small and, 13% are medium. Technical potential for mobile homes is assumed to be null—as theroofs are typically not suitable for solar. These national probabilities are used as weighting
factors for sampling residential buildings by building type.

11

Tracts were determined to be covered if there was at least a 90% spatial intersect requirement with the LiDAR
extent.

9This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 4. Building Type to Building Size Schema
Building Type

Building Size Class 12

Rooftop Area (ft2)

Single-Family Detached

Small

0 to 5,000 ft2

Single-Family Attached

Small or Medium

0 to 25,000 ft2

Multi-Family 2–4 Units

Small or Medium

0 to 25,000 ft2

Multi-Family 5–19 Units

Small or Medium

0 to 25,000 ft2

Multi-Family 19+ Units

Medium or Large> 5,001 ft2

Next, we generate a cross-tabulation of possible building count combinations from the ACS
household counts by building type (e.g., four housing units in 2- to 4-unit buildings could either
be two 2-unit buildings, or one 4-unit building). This array of combinations is narrowed down by
removing Census Blocks reported by the HAZUS model 13 (FEMA 2016) to have no residentialbuildings. We then generate an array of actual building IDs that meet the size criteria of thebuilding-type combinations. 14 In cases where building IDs do not satisfy the criteria of thebuilding-type combinations, 15 we downgrade the building type requirements by the smallest
percentage possible. We then bootstrap actual buildings for each building type using ourrandomly sampled household-to-building count combination as the sampling rate. The sums of
the sampled building technical potentials are generated per tract and the income and tenure
estimates are stratified using a proportional allocation approach.
The process described above is a stochastic process; the final tract-level and national-level
results are determined based on the median of a 100-sample Monte Carlo simulation. The
coefficient of variance between samples for each tract and each city were computed forvalidation purposes, and the tract averages of the 100 runs were calculated. Overall, we found thesampling error to be low, with a mean coefficient of variance of 6.8% and 1.1% for tracts andcities. Further details on the Monte Carlo simulation are covered in Appendix B. The result is adata set of tract-level estimates of suitable buildings and their technical potentials per income
group, building type, and tenure for the 37% of U.S. tracts with LiDAR coverage.
These methods rely on a few key assumptions with notable limitations. One is that the
proportional allocation approach used to estimate the technical potential by income and tenureassumes that LMI and non-LMI groups (or renter and owner groups) residing in the samebuilding type are equally as likely to be suitable. Suitability in this report is based only on thesize, shading, and orientation the roof planes. Other factors such as roof age or roof material,
however, are likely to disproportionately affect low-income households and could affect building
12

Small, medium, and large building sizes area convention defined by Gagnon et al. (2016).
HAZUS is a GIS-based natural hazard analysis tool developed by the Federal Emergency Management Agency.
14
For cases in which the sampling pool of buildings does not satisfy the criteria of the building-type combinations,
we downgrade the building-type requirements by the smallest percentage possible.
15
For any given tract, there are potentially three reasons why the pool of potential residential buildings does notsatisfy the building type combinations: (1) the LiDAR data might be outdated (years vary for each city and range
from 2006–2014) and new builds could have occurred since the LiDAR scan was collected, (2) the HAZUS General
Building Stock reports of residential blocks could be inaccurate or outdated (i.e., residential buildings could exist inblocks reported to only contain non-residential buildings), and (3) the random weighted sampling methodology usedin the ACS county-to-tract building type disaggregation might not accurately reflect the building stock in the tract.
13

10This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

solar suitability. Additionally, due to data limitations, the model is unable to identify building
occupants’ demographics, and this biases intra-tract estimates. That is, although the algorithm
does consider inter-tract variance (i.e., smaller homes in low-income tracts), it cannot distinguishintra-tract variance of building size. Notably, we would expect a positive correlation between
income and building size or, on a per-capita basis, for owner-occupied buildings to be larger thanrenter-occupied buildings. These potential biases are offset by the expectation that demographics
are mostly homogeneous within a tract because a tract contains about 4,000 people. Anotherrelated assumption is that the algorithm does not distinguish between occupants’ incomes ortenures living within the same building.
Other limitations of these methods are related to data availability. The lack of access to anational parcel data set that can be used to identify an individual building’s true characteristics
means that the methods used must rely on FEMA’s HAZUS model (FEMA 2016)—a modeled
block-level general building stock data set to filter blocks without residential buildings.
However, HAZUS itself is an estimated model, and could have zoning inconsistencies.
Additionally, the most-recent version of HAZUS is based on data from the 2000 Decennial
Census and there might be inconsistencies between residential tracts reported in HAZUS and the
2011–2015 ACS data for tracts with new building construction. Finally, not having parcel datathat enables identification of a building’s type (i.e., single-family detached, multi-family 2–4
units) and the roof size of different building types, means relying on a schema of building type tobuilding size. This schema of building type to building size (Table 4) is based on data from the2009 Residential Energy Consumption Survey (RECS) (EIA 2013) to sample buildings for eachbuilding type from a pool of LiDAR building footprints based on the building’s size. Future
work should attempt to use parcel data—including representation of building and roof age—which would permit more-precise identification.
2.3.3. Imputing LMI Technical Potential for Tracts Outside of LiDAR Coverage
To determine the technical potential of the remaining 63% of tracts without LiDAR data wedeveloped a statistical model, trained on the tracts with observed data, 16 to predict tract-level
characteristics. The imputation model used largely follows the same methodological frameworkused in Gagnon et al. (2016, Appendix A–D) but is applied at the tract-level and with additional
processes to disambiguate the number and size of buildings from ACS household totals by numberof units. This adjusted predictive modeling framework consists of four sub-models, namely:
1. Household-to-Building Model,
2. Small Building Suitability Model,
3. Rooftop Tilt and Azimuth Model, and
4. Rooftop Plane Area Model.
Each model predicts median and upper- and lower-bound estimates for individual components
defining the suitable rooftop area. The complete model combines the four component models toestimate median and confidence intervals for tracts without LiDAR coverage. Appendix B
details this four-model predictive modeling framework. Coupled with the LiDAR covered tracts
16

Tracts used for the training set were only used if at least 90% of area was covered by the LiDAR raster extent.
therefore, weighting of tracts by their coverage is not needed for out-of-sample prediction.

11
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

from Section 3.2.2, this provides complete coverage of LMI technical potential across all tracts
in the United States.
There are three main limitations with this imputation approach. First, because the LiDAR area
estimates for residential buildings are based on the median of sampled runs, the imputation
estimates out-of-tract technical potential using modeled inputs. Next, as with the estimates for
LiDAR-covered areas, although counts of households by income at the tract-level were available,
we do not have direct observations of the cross-tabulation of building income-type-tenure
combinations by tract. Instead, these counts are simulated based on the observed proportion at
the county level and using income-tenure relationships. In effect, this means that the model does
not represent inter-tract differences in proportion of, for example, rental versus owner-occupied
buildings within the county. Third, imputed results for rural areas likely are biased because mostof the observed LiDAR data is in urban areas. This bias is mitigated, however, by use of urbanrural isolating variables as independent variables (i.e., locale and land use and land cover) in our
predictive modeling.

2.4. Model Uncertainty and Caution in Interpreting Results
Results presented in this report are based on a series of statistical processes, often with varying
degrees of certainty. For reasons described below, this report is unable to provide 95%
confidence intervals for all of the estimates, although the authors acknowledge thisunintentionally could overstate the certainty of the results. Thus, readers are urged to use caution
when interpreting results—particularly for policy-planning or regulatory considerations. Two
dimensions of uncertainty within the data are highlighted.
2.4.1 Uncertainty of solar potential for LiDAR-covered and non-covered areas.
The primary data used in this report are a series of LiDAR scans of individual buildings for 128
metro regions, spanning 40% of the U.S. population. For these regions the LiDAR data enables
the accurate assessment of the total number of buildings in the tract, their area, and the fractionof buildings with solar-suitable roofs (solar suitability). Summing over all the buildings gives ahigh degree of confidence of the total residential potential in these tracts. Assigning thesebuildings to the residential sector, estimates of the ratio of the number of buildings per building
type, and estimates of building size to building type also makes assumptions documented in the
Methods section. The process uses several sub-models, and the estimates do not have acomposite confidence internal because the data required to quantify the composite error isunavailable. We were able to quantify the error of the LiDAR-covered areas in an indirect way,
however, by rerunning the model 100 times with different random seeds. When doing so, we
found that the average tract-level estimates had a coefficient of variance of 6.8%. That is, thestandard deviation of the 100 runs was 6.8% of the mean estimate. For cities, which arecomposed of several tracts, the mean coefficient of variance was 1.1%. Both statistics giveassurance that the error of the various sampling methods is acceptably low.
We then trained statistical models to impute the gross solar potential for the building stock ineach non-LiDAR tract. By its nature, this statistical process is less accurate than having directlyobserved data. The imputation process uses sub-models to individually estimate the ratio ofhouseholds to buildings, the fraction of solar-suitable buildings, the distribution of rooftop tilts
and azimuths, and the distribution of rooftop plane areas. Because we don’t have direct
12This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

observations of rooftops in the non-LiDAR covered areas we are not able to directly estimate theerror. However, we did validate the statistical model by training it on 75% of the LiDARcovered areas, to test its accuracy when applied to the remaining 25%. Comparing the grosspredicted LMI technical potential for the tract to the actual value shows that 68% of the estimates
are within 10% of the actual values and 90% of the predicted values are within 20% of actual
values. Tract-level variance of this magnitude could be unacceptable for planning purposes,
although the accuracy increases when moving to sequentially larger geographic areas (cities,
counties, states).
2.4.1 Uncertainty of distribution of solar potential by income-tenure-types
A key purpose of this report is to estimate solar technical potential for different income-tenuretype combinations (e.g. 50% to 80% AMI, owner-occupied single-family buildings). The LiDAR
data does not allow us to directly observe the zoning, income level, tenure, or building type ofindividual buildings. To estimate the frequency of these different combinations for tracts with
LiDAR coverage, the LiDAR data is intersected with various Census American Community
Survey data. The justification is that, although the estimate of income-tenure-type for any
individual building cannot be estimated, the accuracy at a tract or county level improves when
averaged over thousands of buildings. For tracts with hundreds of buildings or fewer, then thissource of error could be greater.
Another important note is that the Census does not publish direct observations of income-tenuretype counts at the tract-level. Instead, the estimates herein are compiled from a variety ofsources. At the tract-level, there is observed data on the count of households by income group.
Cross-tabulations of tenure by income, however, and tenure by household size by housing units
in a building all are observed at the county level. To disaggregate to the tract level, we used
proportions of income at the tract level to proportionally disaggregate the income-tenure counts
from the county. These estimates could be biased whenever a specific tract has substantiallydifferent proportions of income-tenure-housing type combinations than the county does. For thisreason, income-tenure tract combinations only are presented in tabular results at the national
level, where the bias should be minimized by aggregation. In the interest of transparency,
however, these values are presented at the tract level in the Solar for All web application and the
REPLICA data set with appropriate disclosure.

13
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3. Results
This section quantifies the technical potential of roof-mounted solar photovoltaic systems,
stratified by the income of the building occupants, tenure, and size of the building. In particular,
this report provides two novel contributions. One is that it estimates the intersection of rooftop
potential with the above building demographics. Another is that the data is aggregated at the U.S.
tract and county-level, instead of by zip code, which is more congruent with Census Bureau,
HUD, and other socio-demographic analyses. In Section 3.1 the quantity and spatial distributionof rooftop technical potential across various building characteristics is estimated. In 3.2 thefeasibility of rooftop solar offsetting 33% of residential electricity consumption is explored, witha focus on the feasibility of reaching parity in adoption levels across income. Finally, in Section
3.3, the report diverges from a national analysis to explore the rooftop technical potential in more
depth in Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C., tounderstand the spatial distribution of potential within these cities as well as the potential on nonresidential buildings that serve low-income populations.

3.1. National Residential Rooftop PV Technical Potential
Over all tracts and all residential buildings in the United States, the gross annual residential
rooftop solar technical potential is nearly 1,000 TWh, or about 75% of residential consumption(DOE EERE 2017). Significant potential is found in every income group (Table 5), with thegreatest overall potential in the non-LMI income group, and with 416 TWh for low-to-moderate(0% to 80% AMI) households over 25.5 million solar-suitable buildings. The LMI opportunity isapproximately 42% of the total U.S. residential potential, indicating that it is a non-trivial portionof the suitable rooftop space. These estimates should be interpreted as the gross technicalpotential of all residential buildings, i.e. they do not consider existing residential PV; in 2016,
approximately 1% of residential buildings have installed rooftop PV (EIA 2017).
The gross technical potential by income group was primarily proportional to the number ofbuildings, though there are differences on a per-capita basis between the groups. Nationally, theaverage technical potential was 14,878 kWh per building. However, the per-building technicalpotential is inversely associated with income (17,106 to 13,711 kWh per building for Very Low
to High) because of the higher rate of occupancy in multi-family buildings among LMI
households, which tend to have larger, flatter roofs than their single-family counterparts. On a
per-household basis, however, the technical potential is positively correlated with income
because the same multi-family buildings have higher household-to-roof area ratios (8,246 to
8,763kWh per household for Very Low to High; 8,553 kWh per household nationally). Taken
together, these results demonstrate that there is significant per-capita technical potential for all
income groups, although—as is demonstrated in Section 3.1.2—much of the LMI potential issited on rental-occupied and multi-family buildings.

14
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 5. National Residential PV Rooftop Technical Potential by Income GroupHouseholds(millions)

Suitable
Buildings(millions)

Suitable
ModuleArea(millions ofm2)

Capacity
Potential(GWDC)

AnnualGeneration
Potential(TWh/year)

Very Low(0%–30% AMI)

19.5

9.4

794.4

127.1

160.8

Low
(30%–50%
AMI)

11.5

5.7

472.8

75.6

95.3

Moderate(50%–80%
AMI)

18.8

10.4

792.0

126.7

159.8

Middle(80%–120%
AMI)

21.1

12.3

900.4

144.1

180.8

High
(> 120% AMI)

46.0

29.4

2,003.3

320.5

403.1

All LMI Buildings

49.8

25.5

2,059.2

329.4

415.9

All Residential Buildings

116.9

67.2

4,962.9

794.0

999.8

Income GroupLMI—

Non-LMI

These estimates are congruent with previous estimates of the total U.S. rooftop technicalpotential, though none of the previous studies explicitly estimate the distribution among income,building types, and tenures. Gagnon et al. (2016), for instance, estimated 1,432 TWh of rooftop
potential across small, medium, and large building types and 926 TWh for small buildings only.
Differences between the results presented in this report and those presented in Gagnon et al.
(2016) can be attributed to differences in scope. Specifically, this report seeks to estimate thetotal residential technical potential, whereas the previous work estimates potential by building
size class (i.e., small, medium, large). Most small buildings are single-family residential (94%)(Gagnon et al. 2016), though not all residential buildings are small; 13% of multi-family
residential buildings are medium or large (EIA 2013). Thus, the estimate for the residential
sector (999.8 TWh) is a mixture of the Gagnon et al. (2016) estimates of small (926 TWh) andmedium and large buildings (506 TWh). In contrast, Denholm and Margolis (2008) estimated419 TWh for the residential sector, though this study relied on coarser methods. Additionally,
these technical potential estimates only include roof-mounted systems on residential buildings;
for context, previous estimates of the technical potential for ground-mounted systems are vastly
larger (281,000 TWh) due to the greater area available for deployment (Lopez et al. 2012).
3.1.1. Spatial Trends in LMI Technical Potential
Figure 2 demonstrates the overall quantity of residential potential by U.S. county in gigawatthours. We find that the quantity of technical potential is highly concentrated amid urban andother densely-populated areas with more building stock. Many of the areas with high levels ofpotential already have significant levels of residential solar deployment (e.g. Arizona, California,

15This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Maryland, Massachusetts, New Jersey), although there also are several states with high solar
potential with currently-low levels of solar deployment (e.g. Illinois, Ohio, Florida,
Pennsylvania, Texas) (GTM 2017)—indicating the opportunity for future growth.

Figure 2. County residential technical potential (GWh)

Figure 3 shows the county-level technical potential, normalized as the percent of county
residential generation potential provided by LMI-occupied buildings. Compared to Figure 2—
which demonstrates the areas with the greatest overall LMI potential—Figure 3 shows areas withdisproportionately high LMI potential after normalizing for population. We find that in 437
counties (14%) LMI technical potential comprises at least half of the county’s rooftop solar
potential. Spatial trends in the fraction of LMI potential are substantially different than those of
the absolute amount. At a high-level, patterns of LMI potential mirror overall income trends in
the United States. LMI potential percentages are greatest in lower-income counties and are also
distinctly higher in rural or semi-rural counties. Areas with disproportionately greater fractionsof LMI potential are seen in the Southeast (i.e., Alabama, Arkansas, Kentucky, Louisiana,
Mississippi, West Virginia) and portions of Midwest and Mountain West.

16
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 3. County LMI rooftop technical potential as percent of total residential potential

Previous national maps (i.e., Figure 2, Figure 3) are represented at the county-level, though theunderlying data is tract-level, because tract-level maps are too finely resolved to show
meaningful spatial trends at a national scale. Tract-level maps, however, are well-suited todemonstrate community-level patterns within a city. As a demonstration we mapped solar
technical potential for LMI households at the tract level for four cities—Chicago, Illinois; San
Bernardino/Riverside, California; and Washington, D.C. (Figure 4), normalized by the tract’s
total residential generation potential. These cities were selected for congruence with aforthcoming companion analysis of the technical, market, regulatory, and social factors affecting
LMI solar markets in those three cities. Unsurprisingly, LMI generation is strongly correlatedwith lower-income neighborhoods in each city. Moreover, the results demonstrate that asubstantial portion of each city’s solar technical potential is in LMI neighborhoods. Therefore,
lower deployment of solar in lower-income neighborhoods would substantially limit the overall
deployment potential in these cities.
For additional analysis opportunities at the city-level, readers are invited to explore theaccompanying web app (https://maps.nrel.gov/solar-for-all), which can be used to explore
additional dimensions of the underlying data.

17
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 4. Percentage of tract-level rooftop solar potential (MW) on LMI buildings

3.1.2. Technical Potential by Tenure, Building Type, and Income
This section discusses a first-of-kind estimate of the distribution of rooftop PV technical potential
by building tenure, housing unit type, and income in the U.S. residential sector. The current U.S.
residential rooftop PV market largely is concentrated among high-income households and forsingle-family owner-occupied homes (Moezzi et al. 2017; Vaishnav et al. 2017). Solar deployment
on renter-occupied buildings is low because of principal-agent issues in the motivation for building
owners to install solar. Namely, because most tenants (88%) directly pay for their utility expenses(EIA 2013), there is reduced incentive for building owners to invest in solar or other building
improvements. Building owners need reasonable confidence that these investments could berecouped through higher rent payments, which could be untenable for affordable housing. Similar
coordination issues exist for multi-family buildings. LMI households face other unique barriers tosolar adoption, including limited access to financing and available funds (Cook and Bird 2017). If
unresolved through policy, market, or regulatory measures, these coordination and financing issues
will inhibit solar deployment on a large fraction of the U.S. building stock.
Figure 5 illustrates the distribution of generation potential by income group and building type.
We estimate that a majority of the residential potential (683 TWh, 68.4%) is situated on singlefamily buildings, as compared to multi-family buildings (316 TWh, 31.6%) and single-family
potential exceeded multi-family potential for each income group. Within LMI households,
however, the distribution is more uniform, with multi-family buildings comprising 40.1% of the
LMI-specific potential.
18
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 5. Generation potential by income group and building type

Similar trends are seen for the segmentation of potential between owner-occupied and renteroccupied buildings (Figure 6) as there is a strong correlation between occupancy in multi-family
buildings and rental status. We estimate that 659 TWh (65.9% of the residential potential) is
situated on owner-occupied buildings, as compared to 341 TWh for renter-occupied buildings(34.1%). For LMI households, potential on renter-occupied buildings (212.7 TWh) slightly
exceeds that of LMI owner-occupied buildings (203.3 TWh).

Figure 6. Generation potential by income group and building tenure

Taken together, Figure 7 shows the intersection of potential by both tenure and building type for
LMI families. The largest modality of potential is for single-family owner-occupied buildings(176.8 TWh), followed closely by multi-family renter-occupied buildings (140.1 TWh). Thoughdeployment of rooftop solar historically has been concentrated on single-family, owner-occupied
19
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

buildings, these results indicate that nearly 60% of potential for LMI buildings exists on theseother underrecognized tenure-type combinations. Table 6 shows the national technical potential
estimates for all income-tenure-type combinations.

Figure 7. Generation potential (TWh) by building type and tenure

20
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 6. National residential PV rooftop technical potential by income group

IncomeGroup

TenureOwnerOccupied
Very Low(0%–30%
AMI)
RenterOccupied

OwnerOccupied
Low
(30%–
50% AMI)
RenterOccupied

OwnerOccupied
Moderate(50%–
80% AMI)
RenterOccupied

Middle
(80%–
120%
AMI)

High
(>120%AMI)

OwnerOccupied

RenterOccupied

OwnerOccupied
RenterOccupied

Building
Type

Suitable
Buildings(millions)

Suitable
Module Area
(millions ofm2)

Capacity
Potential(GWDC)

AnnualGeneration
Potential(TWh/year)

MultiFamily

0.1

38.3

6.1

7.8

SingleFamily

5.1

274.7

43.9

55.7

MultiFamily

1.0

311.8

49.9

62.4

SingleFamily

3.1

169.5

27.1

34.9

MultiFamily

0.1

34.2

5.5

6.9

SingleFamily

3.7

198.3

31.7

40.0

MultiFamily

0.6

168.0

26.9

33.6

SingleFamily

1.3

72.3

11.6

14.9

MultiFamily

0.2

59.6

9.5

11.9

SingleFamily

7.4

402.7

64.4

81.1

MultiFamily

0.7

219.9

35.2

44.1

SingleFamily

2.0

109.9

17.6

22.7

MultiFamily

0.3

84.2

13.5

16.7

SingleFamily

9.7

527.6

84.4

105.8

MultiFamily

0.6

196.4

31.4

39.3

SingleFamily

1.7

92.2

14.7

19.1

MultiFamily

0.8

234.2

37.5

46.2

SingleFamily

25.9

1424.7

227.9

286.8

MultiFamily

0.8

237.7

38.0

47.8

21
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SingleFamily

1.9

106.8

17.1

22.3

3.2. Feasibility of Parity in Solar Adoption Rates
The U.S. Department of Energy Solar Energy Technology Office recently announced updated
cost targets for solar energy of $0.05/kWh for residential-scale solar systems by 2030. Using theRegional Energy Deployment System (ReEDS) capacity expansion model, NREL estimated that
achieving these costs could result in 405 GW of PV capacity in 2030, which would provide 17%of contiguous U.S. electricity generation (Cole et al. 2017). By 2050, deployment could increase
to 971 GW, which would provide 33% of generation. These projections do not explicitly
delineate between utility-scale and distributed-scale solar, nor do they impose constraints ofminimum regional penetration levels.
This report investigates the technical feasibility of reaching parity in adoption rates of distributed
solar in the residential sector, regardless of socioeconomic status. Because of the ambiguity of theannounced goals, the authors interpret this to mean, “Is there sufficient technical potential forrooftop solar to offset 33% of low- and moderate-income household annual electrical consumptionin each U.S. county?” In counties where technical or market barriers prevent reaching theconsumption-offset goal, we also investigate what types of new business models would be required,such as community solar, or virtual net metering (Feldman et al. 2015; Cook and Bird 2018).
To assess the viability of the consumption offset targets we first estimated the total electric
consumption of low- and moderate-income households aggregated by county. To do so, we startwith the Low-Income Energy Affordability Data (LEAD) residential energy expenditure data set
which estimates average annual energy expenditures per Census tract, AMI income group, 17
tenure, and building type (among other breakdowns) (DOE EERE 2017). The LEAD data set isbased off the 2011–2015 American Community Survey Public Use Microdata Samples (PUMS)of self-reported monthly household electricity expenditures. Using this data, the yearlyhousehold expenditure 18 is divided by the average cost of electricity per tract, 19 to get the perhousehold yearly electrical consumption at the tract level. This number then is multiplied by the
number of households in the tract to derive the total residential energy consumption by tract. Due
to high variability within the expenditure data at the tract level, we aggregate these results to thecounty level to gain more confidence in the results. 20 Table 6 demonstrates key national statistics
17

Note that due differences in the way the AMI incomes are grouped in the LEAD data, we are unable to separately
identify electrical consumption for the ‘Middle’ (i.e., 80%–120% AMI) and High (i.e., 120%+) income groups, sothey are reported combined as “Non-LMI” (i.e., 80%+) in this section of the report.
18
A slight mismatch exists when aligning the LEAD data with the data from the present research. This mismatch is due
to differences in disaggregation methods used for each analysis. Though the mismatch is minimal, there are tract
instances where LEAD reports no households (of a building type, tenure, and income group) where we reporthousehold counts. These instances use the state averages of LEAD energy expenditures, per group, to backfill the data.
19
Average cost of electricity per tract was calculated using the 2016 EIA 861 (EIA 2016b) utility survey reporting
total kilowatt-hours sold divided by the number of customers served, per utility and state, for bundled utilities only.
Tracts were geospatially tagged to each utility territory using Ventyx ABB electric utility territory geometries (ABB
Energy 2017) and ties were broken using NREL’s standard methodology. In cases where no data was reported by
the EIA 861 for a tract, the state average was used.
20
Validations of LEAD data were performed at the aggregate by comparing national results to RECS estimates. The
RECS data is insufficiently resolved to permit comparison at the tract or county level.

22This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

of residential energy consumption, where the total residential annual consumption is estimated at
1,325 TWh and the consumption for LMI (0%–80% AMI) households is estimated at 493 TWh.
Table 7. Annual Residential Electricity Consumption by Income, Building Tenure, and Type

Income Group

Building Tenure

Building Type

Annual Electricity
Consumption (TWh)Very Low(0%–30% AMI)

Owner-Occupied

Multi-Family

1.1

Other

8.0

Single-Family

76.6

Multi-Family

41.7

Other

8.2

Single-Family

52.6

Multi-Family

1.6

Other

3.6

Single-Family

51.4

Multi-Family

23.5

Other

2.6

Single-Family

21.1

Multi-Family

3.0

Other

10.3

Single-Family

113.9

Multi-Family

32.7

Other

5.3

Single-Family

35.3

Multi-Family

19.8

Other

47.9

Single-Family

620.2

Multi-Family

67.9

Other

9.3

Single-Family

67.0

Rental-Occupied

Low (30%–50% AMI)

Owner-Occupied

Rental-Occupied

Moderate (50%–80%
AMI)

Owner-Occupied

Rental-Occupied

Non-LMI (80%+ AMI)

Owner-Occupied

Rental-Occupied

Total LMI

492.7

Total Residential

1324.623
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Income Group

Building Tenure

Building Type

Annual Electricity
Consumption (TWh)Very Low(0%–30% AMI)

Owner-Occupied

Multi-Family

1.1

Other

8.0

Single-Family

76.6

Multi-Family

41.7

Other

8.2

Rental-Occupied

The method of matching generation potential to household consumption makes threeassumptions. First, because the LiDAR data does not enable the identification of consumption at
the individual-building level, the analysis does not explicitly consider mismatches of generation
potential and consumption at the individual household level, only in aggregate at the county
level. Multiple factors explain variance in household electrical consumption (e.g., number ofoccupants, building efficiency, fuels used for heating) as well as variance in the building-level
solar suitability (e.g., unshaded roof area, tilt, azimuth). Next, use of the average cost ofelectricity could underestimate the electrical consumption since some utilities offer belowmarket electricity rates for low-income and elderly subscribers. Despite this potential bias,
average electrical rates were used because a county-level database of low-income rates was notavailable. Finally, although use of fuels such as natural gas and fuel oil are a significant portionof energy expenditures for some households, the present work only considers offsetting existing
electrical consumption; considering the electrification of these end uses is beyond the scope of
this study.
Using these techniques, Figure 8 compares estimates of the solar technical potential withelectrical consumption by income group, for the single-family owner-occupied (SFOO) building
stock only. We start with this intentionally conservative comparison because, as describedearlier, there are currently significant barriers to deploying residential solar on renter-occupied
and multi-family buildings. Under this assumption we find that offsetting at least 33% of LMIelectrical consumption with rooftop solar only on SFOO buildings is technically feasible on anational scale for all income groups except the Very-Low (0% to 30% AMI) group. However,this feasibility belies a few practicalities. First, it implies buildout of nearly all SFOO roof
space—an impractical and unforgiving market challenge. Second, deployment on SFOO
buildings alone implies that residents of renter-occupied and multi-family buildings would not be
served, counter to the goal of increasing widespread solar access. Notably, the fraction of
consumption that can be offset with SFOO buildings is inversely correlated with income group
because of lower occupancy in SFOO by LMI households. This means that deployment on
SFOO-only buildings leads to lower consumption-offset levels for the lowest-income
households. We note that these calculations exclude the effects of roof age or measures needed to
improve roof structural suitability as these were classified as economic, rather than technical,
factors.

24
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 8. Comparison of fraction of consumption met by generation potential for single-family
owner-occupied buildings alone, by income group

Next, we estimate the feasibility of solar consumption offset targets when considering the entire
residential building stock. We find that there is more than sufficient roof space, on a technicalbasis, to offset 33% of LMI electricity consumption, although to do so would require deployment
on rental-occupied and multi-family buildings. Deployment on these classes of buildings likely
would require development of new business models or other regulatory changes. Figure 9
demonstrates the combined national technical potential for the four tenure-type combinations,
single-family owner-occupied (SFOO), single-family renter-occupied (SFRO), multi-family
owner-occupied (MFOO), and multi-family renter-occupied (MFRO), as compared to theelectricity consumed for each group, both on a national basis. For the Very Low, Low, Moderate,and Non-LMI income groups the percent of generation potential to electrical consumption is85%, 91%, 80%, and 70%, respectively. Due to lower per-capita levels of consumption for lowincome households, the fraction of consumption that can be offset is greater for LMI households
than for non-LMI households. Note that none of the income groups are projected to have atechnical potential that exceeds the group’s consumption level.

25This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 9. Technical feasibility of matching residential electrical consumption with rooftop solar, byincome group•

Though there is sufficient roof space on a national basis to offset 33% of electricity
consumed for LMI buildings, the roof space is insufficient in all counties. Table 8
demonstrates, on a national basis, the sum of the discrepancy between generation
potential and a 33% offset for each building type for LMI households. Shortfalls are mostsignificant for mobile homes (38 TWh)—whose roofs are typically not structurally
suitable for solar, followed by single-family (6 TWh) and multi-family (2 TWh)buildings. Therefore, many occupants of mobile homes—who are disproportionately lowincome—face significant challenges in gaining access to solar energy.

Table 8. Estimates of National Shortfall in Rooftop Solar Generation Needed to Nationally Offset
17% and 33% of LMI Consumption
Goal

Building TypeGWh Needed

17%

Multi-Family

789.9

Single-Family

1,224.8

Other 2138,026.8

Multi-Family

1,905.2

Single-Family

6,085.5

Other38,026.8

33%

21“Other” building types primarily include mobile homes, but also include other miscellaneous residential electricity
uses (e.g., boats, recreation vehicles).

26
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 10 shows the maximum ratio of LMI electrical consumption that could be offset withrooftop solar from all LMI buildings in the county. When allowing deployment on LMI rentaloccupied and multi-family buildings, there are 31 counties with insufficient technical potential tooffset at least 33% electrical consumption, or about 3 TWh shortfall. When allowing deployment
only on single-family owner-occupied LMI buildings (Figure 11), however, there are 1,257
counties (40% of all U.S. counties) with insufficient potential to reach at least 33% consumption
offset, or about 202 TWh shortfall. Again, note that the statistics in Figure 10 and Figure 11
represent the average penetration achievable for the entire building stock in the county; there arelikely to be significant variance in the achievable penetration for individual buildings based on
the roof characteristics and occupants’ energy use.
Spatial trends in generation penetration largely mirror regional variation in per-capita electricity
consumed, primarily due to which fuels are used for building heating and cooling loads. For
instance, per capita electricity consumption is greater in the Southeast because electricity is the
predominant fuel for heating and cooling loads in that region, whereas natural gas is the
predominant fuel in California and Southwest, and fuel oil is predominant for the Northeast. In
effect, this means that more solar generation is needed to offset the 33% of consumption in theSoutheast, but less is required in California, the Southwest, and the Northeast.

Figure 10. Percent of LMI electrical consumption that can be offset by rooftop solar generation(county)—all LMI buildings

27This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. Percent of LMI electrical consumption that can be offset by rooftop solar generation(county)—single-family owner-occupied LMI buildings only

From a technical basis alone, there is sufficient rooftop space on LMI buildings for at least 33%of their electrical consumption to be met with rooftop solar generation. However, reaching thispenetration level would require alternative deployment models than commonly found today.
These models should address coordination issues inherent to rental-occupied and multi-family
buildings as well as LMI financing and affordability barriers (Cook and Bird 2018). Such models
should also ensure that rental property owners are incentivized to install solar on theirbuildings—for example, by bundling utility expenses with rent payments as a means of passing
through savings to the renter. Alternatively, various shared solar models (Feldman et al. 2015),
including virtual net metering, could be effective in allowing building owners to sell rooftop
generation directly to their occupants. Similar issues exist for multi-family buildings, where
shared solar or community solar models could help to address tenant-owner coordination issues.
Some of the coordination issues, however, might be too intractable to resolve. For this reason,
this report also includes penetration statistics considering only single-family owner-occupied
buildings, which essentially identifies the quantity of off-site solar generation (e.g., community
solar or utility-scale solar) that is needed for the penetration target.
Calculations of the potential for solar generation to offset electrical consumption are based on
annual generation and consumption levels and do not consider potential mismatches at the hourly
level. For many buildings, and without energy storage, these mismatches are likely to be
substantial. For instance, buildings might generate more energy than consumed during midday,
when irradiance is highest, resulting in excess energy exported to the grid. In contrast, during
28
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periods of peak demand (e.g., afternoons and evenings), consumption could exceed generation,
resulting in energy delivered from the grid. There are a few important implications of thisphenomena for policymakers. One is that retail tariff design and other associated policies
influence the economic value of distributed solar to LMI households, particularly, the value ofexcess generation exported to the grid. Without careful thought, this could limit the nominal
purpose of LMI programs—energy burden reduction. Another is that these consumption andgeneration mismatches, sans energy storage, limit the ability for distributed solar to provide
resiliency benefits, which also could be a policy goal. Finally, LMI solar should be consideredmore broadly in the perspective of transmission and distribution grid-integration issues, such as
the energy and capacity value of solar energy, potential for solar overgeneration, and the hosting
capacity of local distribution networks. For more details on some of these issues see Denholm etal. (2016) and Palmintier et al. (2016).
3.2.1. Additional Factors Impacting Building Technical Suitability
Although the LiDAR method for assessing technical potential accounts for several importantfactors that can affect rooftop suitability (e.g., unshaded area, tilt, azimuth), it excludes otherfactors that also might matter. For instance, it is generally not advised to install solar panels onolder roofs, because it is costly to uninstall solar panels prior to replacing the roof. Thus,
although roof age is categorized as an economic factor and not a technical factor, it impacts thebuilding suitability and overall market potential. Other salient factors that could impact abuilding’s suitability can include electrical code or interconnection issues, unsuitability of theroof to support the weight of the panels and racking (e.g. flat roof without enough support or amobile home), and existing violations of building codes that are cost-prohibitive to address in the
installation.
To estimate what proportion of LMI buildings might face these types of challenges, we analyzed
historical data from GRID Alternatives installations, a non-profit provider of solar to low-income
households. Because the data are collected primarily for single-family homes in California, thedescriptive statistics are not intended to be representative of other geographies or for multifamily buildings. Nevertheless, these data provide a first-of-kind insight into the frequency ofadditional barriers to solar deployment. Determining the economic feasibility of preparing a roof
for solar installation often requires physical inspection and is beyond the scope of this report toconclusively address.
Based on a sample of 24,269 potential solar installations (“leads”), we identified 10 reasons why
a building was determined—either by physical inspection or in conversation—to be unsuitable
for solar: Old roof (n = 1,474), excessive (unspecified) upgrades required (n = 552), buildingcode issues (n = 340), roof shading (n = 314), electrical service issues (n = 187), unsafe roof
pitch (n = 152), insufficient roof space (n = 137), mobile home (n = 117), roof orientation (n =47), and roof type (n = 31). Of these 10 reasons, “roof space,” “roof orientation,” “roof shading,”and “unsafe roof pitch” are excluded, as these factors already are considered in the LiDAR
method. “Mobile home” also is excluded because these buildings already were excluded from thepresent technical potential analysis. Additionally, of the 24,269 leads, 10,781 were excluded for
non-technical reasons (disinterest in solar n = 4,224; ineligibility to receive low-income solar
grants n = 6,557) and did not receive technical evaluations, so it is undetermined whether theseleads would have been technically suitable. Thus, it’s estimated that 10.5% to 18.9% of low-29
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

income buildings could face additional techno-economic barriers to solar beyond thoseconsidered in the LiDAR method alone. Readers are urged to use caution in interpreting this data
due to representativeness issues. Future work should couple physical inspections with technicalpotential assessments.

3.3. Technical Potential for Select Buildings that Serve LMIHouseholds
Next, we investigate the possible role for third-party buildings that serve low-income populations
to host solar on their roofs. Solar deployed on these third-party buildings could either reduce
operational costs, thereby helping to expand mission-related activities, or capacity could
explicitly be designated to offset local electrical consumption via a virtual net meteringarrangement. The specific building classes investigated are public sites (e.g., libraries, police
stations, local government), public housing (i.e., federally assisted rental housing), K–12 publicschools, homeless shelters, and places of worship. These building classes are not intended to becomprehensive of all third-party buildings that could deploy solar. Results for this section arepresented for the three cities analyzed in greater depth in the forthcoming companion report(Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C.). Methods forcollecting the data involved manual effort (see Appendix D), so at this time we are unable topresent a national assessment of the five building types.
To quantify the technical potential of LMI-serving buildings in the cities, we obtained GIS data
for each building type and spatially tagged these layers to the LiDAR buildings database. The
LMI-serving building data is an agglomerate data set from a variety of sources. Because the dataconsists of varied data sets and formats, multiple methods were used to tag each building
geometry to one or more LiDAR building footprint. Jointly, this enabled identification of thetotal technical potential for each building type in each city. Appendix D provides further detailon methods and data sources.
Over the five building classes, schools have the greatest technical potential opportunity by far,although there are substantial opportunities for all the classes examined (see Table 8).
Collectively the gross generation potential for the five building classes is estimated to be
1,012.2 GWh, 311 GWh, and 377.1 GWh for Chicago, San Bernardino/Riverside, andWashington, D.C. respectively. These generation estimates are based on the solar suitability
criteria (i.e., non-North facing) and do not include the possible on-site area available for groundmounted systems. To put these values in context, they represent approximately 13%, 29%, and
9% of the estimated annual electricity consumption of low- to moderate-income households inthe city.

30
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Table 9. Annual Generation Potential for Select Third-Party Buildings in Chicago,
Washington D.C., and San Bernardino/Riverside (GWh)
ChicagoSan Bernardino/ Riverside

Washington, D.C.

Public Housing

62.8

16.6

31.0

Public Sites

51.9

24.422

41.1

K–12 Schools

306.4

106.2

73.0

Homeless Shelters

5.3

0.6

5.6

Places of Worship

108.1

7.3

32.8

Total (GWh)1072.3

311.0

377.1

Next, on-site electrical consumption of each of the third-party buildings is estimated to determine
the feasibility of oversizing a PV system with the purpose of using excess solar generation tooffset nearby LMI households. To estimate the on-site consumption, each building was matchedwith a comparable representative building in the EIA Residential and Commercial Building
Energy Consumption Surveys and—to match local climate-related consumption patterns—to thenearest Census Division (EIA 2013; EIA 2016a). This allows us to derive the average electricalconsumption per square foot of building footprint for each building type, which then ismultiplied by the building footprint of each of the third-party buildings in the LiDAR database.
When sizing the PV system for each building the same suitability criteria is used (e.g., no
installation on north-facing roof planes), and it is assumed that each building offsets 100% of its
on-site consumption before exporting generation to the community. Finally, this method only
considers generation and consumption on an annual basis and does not consider potential
mismatches in the hourly profiles. Appendix D describes this process in more detail.
We find that, after accounting for on-site consumption, the five third-party building types
considered could feasibly oversize PV systems on their roofs such that excess generationaccounts for 1.3% to 8.7% of LMI consumption in the city (Table 9), and the opportunity
differed substantially between cities. Due to the greatest solar resource of the three regions, San
Bernardino/Riverside had the highest generation excess fraction, and it is estimated that thirdparty buildings’ excess could account for 8.7% of LMI consumption. For Washington, D.C.—thesmallest fraction—it is estimated third-party buildings excess could account for 1.3% of LMI
consumption.
Schools, followed by Places of Worship were consistently the highest opportunity of the fivebuilding classes considered. Both buildings typically are in residential neighborhoods. Schools
usually have large flat roofs and low consumption in the summer, when solar irradiance ishighest. Places of Worship had low levels of electricity consumption and moderately favorable
roof characteristics. The results also indicate that Public Housing, Public Sites, and Homeless
Shelters likely have insufficient rooftop space to offset 100% of their own on-site consumption—
though actual values could vary for individual buildings.

22

Due to data limitations, generation potential for San Bernardino public sites was not estimated.

31
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 10. Net Excess Rooftop Potential Accounting for Building Self-Consumption by
Building Class and City
ChicagoSan Bernardino/ Riverside

Washington, D.C.

Public Housing (GWh)

0.0

0.0

0.0

Public Sites (GWh)

0.0

0.0

0.0

K-12 Schools (GWh)

19.8

19.6

5.5

Homeless Shelters (GWh)

0.0

0.0

0.0

Places of Worship (GWh)

31.5

3.2

4.6

Total (GWh)

51.4

22.8

10.1

Total (% of LMI
Consumption in City)

2.1%

8.7%

1.3%

32
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4. Conclusion
This report presents a first-of-kind assessment of the technical potential of rooftop solar for lowand moderate-income households and provides insight on the distribution of solar potential bytenure, income, and other building characteristics. The research indicates that a substantial
fraction (42%) of the national residential rooftop solar potential is located on LMI buildings and,for all incomes, a substantial fraction is located on multi-family and renter-occupied buildings.
We also find that rooftop solar can significantly contribute to meeting U.S. electricity demand.
Specifically, from a technical basis there is sufficient rooftop space to ensure that at least 33% of
U.S. residential electrical consumption is offset by rooftop solar—although to do so requires
deployment on multi-family and renter-occupied buildings. Reaching this level of offset is mostfeasible in Northeast and Southwest portions of the United States, where per-capita electricity
use is lowest. This report also highlights the importance of ensuring solar access for mobile
home residents, who are disproportionately low-income yet typically cannot install rooftop solar
because of structural concerns.
In cases where LMI households are unable to adopt solar for their own homes, non-profit entities
might enter agreements to host solar on their own roofs. This study investigated the feasibility
for five classes of buildings in Chicago, San Bernardino/Riverside, and Washington, D.C.,
finding that the buildings likely could oversize PV systems on their roofs equivalent to offsetting
1.3% to 8.7% of local LMI consumption. This fraction is significant but is unlikely to be aleading mechanism for increasing LMI access to solar. Future research could explore thefeasibility of ground-mounted systems at strategic locations (i.e., community solar) or
installation on for-profit buildings.
Traditional deployment models have insufficiently enabled access to solar for low- andmoderate-income households. In part, this is because there are significant economic and financial
barriers to LMI solar adoption without policy action. These include poor access to credit, lowcapital availability, housing uncertainty, and many more. Without innovation either inregulatory, market, or policy factors, a large fraction of the U.S. potential is unlikely to be met.
Potential electric bill savings from rooftop solar would have the greatest material impact on the
lives of low-income households as compared to their high-income counterparts and could help
mitigate the energy burden faced by these households.

4.1. Data for Public Use
This report ultimately seeks to provide objective data for regulators, policymakers, nonprofits,
and project developers to make informed decisions that are best for their own communities, forexample to assess the potential for rooftop solar in their jurisdictions and perform policy costbenefit analysis. To this effect, data used in this report is provided freely via NREL’s website in
two formats. One format is an interactive web application using NREL’s OpenCarto platform,
and was developed to enable user to browse, visualize, and export results(https://maps.nrel.gov/solar-for-all). Secondly, the technical potential data used in this report,
accompanied by several additional techno-economic variables, and aggregated at the tract-level,is available for download at (https://data.nrel.gov/submissions/81). The Rooftop Energy Potentialof Low Income Communities in America (REPLICA) data set includes measures of electricity

33
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

expenditures ($/month), demographics, utility electric rates, and other important variables
compiled from a variety of sources and tagged to each Census tract.

4.2. Future Work
Three topics for future research are suggested to extend and improve this work. First, this work
uses limited building-level attributes within the analysis, except for rooftop planes obtained via
LiDAR. Many local governments, however, increasingly make building-level GIS files available
for free or for a modest fee (i.e., tax rolls). Building-level models would permit substantiallymore-nuanced assessments of the technical, economic, market, and behavioral aspects of rooftop
solar deployment.
Future research also could improve the representation of low-income energy consumption,
perhaps also at the building level. This analysis does not identify building-level variation in
consumption, which is significant. We also acknowledge our limitation in not considering hourly
mismatches between consumption and solar generation, which can be significant. Thesemismatches—and the value of excess generation exported to the electric grid—are an importantdimension of the economic evaluation.
Finally, although this analysis does identify differences in technical potential between singlefamily and multi-family buildings, there is room for improvement. Specifically, future research
could use building-level data sets to more precisely identify the locations of multi-family lowincome buildings. Future work also could improve the representation of tenant-level electricalloads versus communal building loads, as well as publish additional data distinguishing how
income affects electricity consumption patterns.

34This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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Appendix A. Allocation of Demographic Attributes
This appendix describes the series of data transformation steps used to identify cross-tabulations(i.e., “crosstabs”) of demographic attributes at the Census tract level by income, building type,
and tenure. This process relies on a set of tables (Table 3) from the 2011–2015 5-year American
Community Survey (ACS). In general, we rely on four major steps when generating a tract-levelcrosstab of household counts by income, building type, and tenure, namely:
1. Calculate the number of households belonging to each $1,000 income group, at the tractlevel.
2. Disaggregate from county data to estimate the number of households, per $1,000 income
group, per tenure, at the tract level.
3. Disaggregate from county data to estimate the number of households, per $1,000 income
group, per tenure, per dwelling type, per household size.
4. Aggregate tract estimates by AMI income group. Create final data set of number ofhouseholds by AMI income group, by building type, and by tenure, per tract.
Each of these steps involves a series of data transformations and relies on a set of statisticalsampling assumptions. Described below are these methods and assumptions for each step inmore detail.

A.1. Calculate the Number of Households Belonging to Each
$1,000 Income Group, at the Tract Level
In the first step of the ACS data processing methodology, we use the tract level “Household
Income in the Past 12 Months (in 2015 Inflation-Adjusted Dollars)” ACS table, which provides
tract-level household counts classified according to ACS income bins. 23 We take this table anddeconstruct the ACS predefined income brackets into $1,000 increments. To do so, we randomly
sample $1,000 increments for each bin assuming that income is uniformly distributed within thebin. We also assign a maximum income cap of $1,000,000/year because no identification in our
results is needed at this resolution.
Disaggregating these ACS defined income bins into $1,000 increments serves two purposes.
First, it allows us to reconcile ACS tables with different income binning (e.g. $35,000–$39,000
versus $35,000–$49,000 reported in the county data set). Second, it allows us to more precisely
allocate incomes to different AMI income groups.

23

ACS tract-level incomes are classified into 14 bins, namely: $0–$10,000, $10,000–$14,000, $15,000–$19,000,$20,000–$24,000, $25,000–$29,000, $30,000–$34,000, $35,000–$39,000, $40,000–$44,000, $45,000–$49,000,$50,000–$59,000, $60,000–$74,000, $75,000–$99,000, $100,000–$124,000, $125,000–$149,000, $150,000–$199,000, and $200,000 or more.

39
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A.2. Disaggregate from County Data to Estimate the Number ofHouseholds, per $1,000 Income Group, per Tenure, at the Tract Level
Next, we disaggregate county demographic crosstabs to the tract level, using the tract household
counts as the disaggregating factor. We start with the “Tenure by Household Income in the Past
12 Months (in 2015 Inflation-Adjusted Dollars)” ACS table at the county level. To disaggregate
this county data to get the estimates at the tract level, we rely on a random-weighted sampling
method, weighted by the total household counts per income bin. Because we do not have thedesired income by tenure data at the tract level, we rely on these random-weighted methods tohelp us disaggregate. In doing so, we do not directly assume that the county-to-tract counts areproportional. Rather, we assume that there is some variability across the component tracts, but
this variability is inevitably weighted by tract household counts per income. This approachallows us to obtain tract-level estimates of the number of households, per $1,000 income group,
per tenure.

A.3. Disaggregate from County Data to Estimate the Number ofHouseholds, per $1,000 Income Group, per Tenure, per Dwelling Type,per Household Size
This next process is similar to that described in A.2 above. Here we rely on two ACS countylevel tables, namely, “Tenure by Units in Structure” and “Tenure by Household Size by Units in
Structure.” We first combine these two tables using a proportional allocation method, joining
based on the shared tenure field. Doing so gives us a county cross-tabulation of tenure, by units
in structure, by household size. We use this county crosstab to then disaggregate to the tractlevel. Using the data set created in the previous step (A.2), we use a random-weighted sampling
methodology to disaggregate the county crosstab. Similar to A.2, we do not assume that thecounty-to-tract disaggregation is directly proportional to the total household counts. Instead, weknow there is variability across tract components, therefore, we use the random-weighted
methodology to allow for variation, but ultimately, weighted by total counts.

A.4. Aggregate Tract Estimates by AMI Income Group
Using this large tract-level crosstab described above (Section A.1 though Section A.3), we assign
the AMI income group break limits to classify the data according to our five AMI income
groups: Very Low Income (0% to 30% AMI), Low Income (30% to 50% AMI), Moderate
Income (50% to 80% AMI), Middle Income (80% to 120% AMI), and High Income (>120%
AMI). To do so, we join the “2016 HUD Income Limits” (HUD 2016) table to each row in ourdata set based on the Census Tract location, household size, and the $1,000-increment binned
yearly household income. With the data categorized into our five income groups, we remove thehousehold size category and aggregate the final data set by the income group, building type, andtenure.

40
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Appendix B. Validation of Monte Carlo Simulations for
Building SamplingThis analysis uses a stochastic process to sample residential buildings in LiDAR covered Census
Tracts (Section 2.3.2) to derive tract-level estimates. As such, we rely on a Monte-Carlo
methodology to generate probability distributions of tract-level technical potential, per building
type. The workflow is run 100 times, each using a different random seed, and the median value
over the 100 runs is used as the final estimate to obtain a more accurate estimate of the technicalpotential in each tract.
To validate our sampling methodology and better understand the variability of the technicalpotential estimates, we calculate the coefficients of variance across Monte Carlo runs for tracts
and cities; city coefficients of variance are detailed in Table B-1. For each run, we calculate thetract and city technical potential averages. Doing so, we find that both the average tract and city
errors are low, 6.8% and 1.1% for tracts and cities, respectively. When plotting these error ratios
at the tract level (Figure B-1), we find that the distribution is lognormally distributed, indicating
most tracts have relatively high levels of certainty (low levels of variability) across sampled runs.
The low coefficient of variance helps to validate the results and give confidence that the
sampling methodology estimates technical potential of residential buildings per tract andbuilding type with minimal error.

Figure B-1. Density function of coefficient of variance in the tract-level technical potential, for all
tracts with LiDAR coverage 24

24This graph plots the distribution of the coefficient of variance of technical potential for each tract with LiDAR
coverage. Each point in the plot represents the coefficient of variance for a single tract across the 100 runs. The data
is graphed using a Kernel Density Estimation (KDE) plot, a non-parametric technique to estimate the probability
density function and visualize the underlying distribution of a continuous variable (Rosenblatt 1956). Notably,
because of smoothing used by the plotting library, the x-axis is not constrained to the minimum 0 bound.

41
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Table B-1 demonstrates the mean technical potential over the 100 Monte Carlo runs for each citywith LiDAR coverage and the standard deviation. Notably, there is more uncertainty in smaller
cities (e.g., Frankfort, KY) with fewer Census Tracts than the larger metropolitan cities (e.g.,
New York, NY). This result is intuitive because model error is the greatest at the tract level andreduced when tract-level estimates are aggregated over larger areas.
Table B-1. Assumptions for PV Performance Simulations Summary Statistics for City-Level
Technical Potential 25
LiDAR City

LiDAR State

Mean (GWh)

STD (GWh)

Coefficient of
Variance

Albany

NY

1.9

0.015

0.008

Albuquerque

NM

3.2

0.022

0.007

Allentown

PA

2.0

0.018

0.009

Amarillo

TX

2.2

0.025

0.012

Anaheim

CA

3.9

0.060

0.015

Atlanta

GA

1.0

0.029

0.028

Augusta

GA

2.7

0.029

0.011

Augusta

ME

1.8

0.078

0.043

Austin

TX

2.2

0.024

0.011

Bakersfield

CA

3.0

0.014

0.005

Baltimore

MD

1.4

0.009

0.007

Baton Rouge

LA

3.7

0.040

0.011

Birmingham

AL

1.6

0.029

0.017

Bismarck

ND

2.3

0.036

0.016

Boise

ID

3.3

0.025

0.008

Boulder

CO

1.8

0.032

0.018

Bridgeport

CT

1.8

0.023

0.013

Buffalo

NY

2.0

0.013

0.007

Carson City

NV

2.3

0.091

0.039

Charleston

SC

1.5

0.024

0.016

Charleston

WV

1.9

0.022

0.012

Charlotte

NC

2.4

0.026

0.011

Cheyenne

WY

2.3

0.040

0.018

Chicago

IL

1.3

0.006

0.004

25

The LiDAR cities listed in Table B-1 do not necessarily align with the legal footprints of city or metropolitan areaboundaries. Rather they refer to the focal city covered in the LiDAR raster extent. A single LiDAR city extent can
span multiple cities and states (e.g. Washington D.C. covers much of Northern Virginia and southern Maryland
cities in addition to the District of Columbia) or they can only span partial cities (e.g. Richmond, VA, is only 55%
covered by its LiDAR raster extent).

42
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LiDAR City

LiDAR State

Mean (GWh)

STD (GWh)

Coefficient of
Variance

Cincinnati

OH

1.5

0.011

0.008

Cleveland

OH

1.2

0.008

0.006

Colorado Springs

CO

2.5

0.014

0.006

Columbia

SC

2.2

0.031

0.014

Columbus

GA

2.7

0.031

0.011

Columbus

OH

2.2

0.016

0.007

Concord

NH

1.8

0.070

0.039

Corpus Christi

TX

2.4

0.052

0.022

Dayton

OH

2.2

0.019

0.009

Denver

CO

2.0

0.010

0.005

Des Moines

IA

2.2

0.031

0.014

Detroit

MI

1.2

0.007

0.006

Dover

DE

2.6

0.075

0.029

El Paso

TX

3.4

0.028

0.008

Flint

MI

2.1

0.019

0.009

Fort Wayne

IN

1.6

0.027

0.017

Frankfort

KY

3.0

0.083

0.028

Fresno

CA

2.4

0.014

0.006

Fort Belvoir

DC

1.7

0.044

0.026

Grand Rapids

MI

3.8

0.051

0.013

Greensboro

NC

2.4

0.025

0.011

Harrisburg

PA

2.9

0.023

0.008

Hartford

CT

1.6

0.014

0.009

Helena

MT

2.2

0.045

0.020

Houston

TX

3.0

0.017

0.006

Huntsville

AL

3.9

0.026

0.007

Indianapolis

IN

2.6

0.018

0.007

Jackson

MS

1.9

0.024

0.013

Jacksonville

FL

2.5

0.026

0.011

Jefferson City

MO

4.2

0.106

0.025

Kansas City

MO

1.7

0.012

0.007

Lancaster

PA

2.9

0.038

0.013

Lansing

MI

2.3

0.036

0.016

Las Vegas

NV

1.8

0.011

0.006

Lexington

KY

2.5

0.024

0.010

43
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LiDAR City

LiDAR State

Mean (GWh)

STD (GWh)

Coefficient of
Variance

Lincoln

NE

1.8

0.022

0.012

Little Rock

AR

2.0

0.026

0.013

Los Angeles

CA

2.3

0.007

0.003

Louisville

KY

1.8

0.045

0.025

Lubbock

TX

2.8

0.030

0.011

Madison

WI

2.2

0.022

0.010

McAllen

TX

5.6

0.038

0.007

Miami

FL

3.1

0.009

0.003

Milwaukee

WI

1.7

0.013

0.008

Minneapolis

MN

1.7

0.011

0.007

Mission Viejo

CA

3.5

0.024

0.007

Mobile

AL

1.1

0.021

0.019

Modesto

CA

3.0

0.037

0.013

Montgomery

AL

3.0

0.037

0.012

Montpelier

VT

1.8

0.058

0.032

New Haven

CT

2.1

0.021

0.010

New Orleans

LA

2.3

0.008

0.004

New York

NY

0.7

0.003

0.004

Norfolk

VA

2.0

0.010

0.005

Oklahoma City

OK

2.7

0.014

0.005

Olympia

WA

3.1

0.032

0.010

Omaha

NE

2.1

0.013

0.006

Orlando

FL

3.2

0.032

0.010

Oxnard

CA

3.4

0.028

0.008

Palm Bay

FL

5.9

0.029

0.005

Pensacola

FL

3.4

0.037

0.011

Philadelphia

PA

1.6

0.007

0.004

Pittsburgh

PA

1.3

0.008

0.006

Portland

OR

2.1

0.011

0.005

Poughkeepsie

NY

2.0

0.016

0.008

Providence

RI

1.6

0.014

0.009

Raleigh-Durham

NC

1.9

0.019

0.010

Reno

NV

2.0

0.027

0.013

Richmond

VA

0.8

0.019

0.024

Rochester

NY

1.4

0.019

0.013

44
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LiDAR City

LiDAR State

Mean (GWh)

STD (GWh)

Coefficient of
Variance

Sacramento

CA

2.2

0.013

0.006

Salem

OR

3.1

0.029

0.009

Salt Lake City

UT

2.1

0.018

0.009

San Antonio

TX

3.4

0.014

0.004

San Bernardino–Riverside

CA

2.9

0.016

0.006

San Diego

CA

2.1

0.013

0.006

San Francisco

CA

2.5

0.009

0.004

Santa Fe

NM

3.2

0.043

0.013

Sarasota

FL

7.3

0.039

0.005

Scranton

PA

1.4

0.018

0.013

Seattle

WA

1.5

0.012

0.008

Shreveport

LA

2.6

0.028

0.011

Spokane

WA

1.5

0.014

0.010

Springfield

IL

2.1

0.023

0.011

Springfield

MA

1.1

0.018

0.016

St Louis

MO

2.4

0.011

0.005

Stockton

CA

2.4

0.026

0.011

Syracuse

NY

1.4

0.021

0.015

Tallahassee

FL

2.2

0.032

0.015

Tampa

FL

2.4

0.023

0.010

Toledo

OH

1.0

0.014

0.014

Topeka

KS

2.3

0.028

0.012

Trenton

NJ

1.5

0.027

0.018

Tucson

AZ

3.2

0.022

0.007

Tulsa

OK

2.7

0.020

0.007

Washington

DC

1.7

0.006

0.003

Wichita

KS

1.9

0.020

0.011

Winston-Salem

NC

2.2

0.030

0.014

Worcester

MA

1.5

0.021

0.014

Youngstown

OH

2.2

0.024

0.011

45
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Appendix C. Predictive Modeling Framework for
Imputing Solar Suitability
This appendix describes the predictive modeling framework used in this analysis to impute solar
suitability for the 63% of Census Tracts without LiDAR coverage. The overall framework builds
off the original methodology developed by Gagnon et al. (2016) to impute solar suitability forsmall, medium, and large buildings in zip codes without LiDAR data. The approach here extends
this work by increasing the spatial fidelity of the statistical model used for imputation (i.e., tracts
versus zip codes) and adding an additional submodel to aid in predicting suitability acrossresidential building types, per income, building type, and tenure. In total, there are four
submodels used for the imputation.
1. Household-to-Building Model
2. Small Building Suitability Model
3. Rooftop Tilt and Azimuth Model
4. Rooftop Plane Area Model
The following sections explore each submodel in greater detail.

C.1. Household-to-Building Model
The household-to-building model is a composite model that estimates the number and size ofbuildings (i.e., small, medium, large) for each tract outside of the LiDAR covered extents. The
model is comprised of two submodels: (1) the household-to-building-count model, and (2) thebuilding-type-to-building-size model. Both submodels rely on a distribution sampling approach
for imputing values from simulated LiDAR estimates.
C.1.1. Household-to-Building-Count Ratios
The household-to-building-count submodel uses the ACS-derived tract-level crosstab ofhousehold counts to estimate number of buildings per building type for an imputed tract based on
the number of households in the tract. Ratio distributions are created for multi-family buildings
only. For single-family buildings, we assume a one-to-one ratio of number of households tonumber of buildings. This study does not account for vacant or seasonal housing. For multifamily buildings, we calculate the mean household-to-building-count ratio for 2–4-unit, 5–19
unit, and 20+ unit multi-family buildings in LiDAR covered Census Tracts. These mean ratios(Table C-1) are used to impute tracts outside of the LiDAR coverage and translate household
counts to building counts.
Table C-1. National Mean Household-to-Building Ratio
Building Type

Ratio

Multi-Family 2–4 Units

3.28

Multi-Family 5–19 Units

9.14

Multi-Family 20+ Units

80.0

46
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C.1.2. Building-Type-to-Building-Size Ratios
The building-type-to-building-size submodel translates the building counts from Table C-1 into
counts by building size (e.g. small, medium, large). Categorizing building counts by size aligns
our data set for national imputation using the methods developed by Gagnon et al. (2016). Likethat shown above, this model is performed on multi-family buildings only; for single-family
detached, we assume all buildings are small. For multi-family buildings, we compute national
means of the smallest size ratios for each multi-family building type and use these to sample
sizes in Census Tracts without LiDAR data. For 2–4 unit and 5–19-unit multi-family buildings,
we take the mean of the percent of the buildings that are small (compared to those that are
medium sized), and for 20+ unit buildings, we take the mean of the percent that are medium(compared to those that are large sized). These national averages of building size ratios perbuilding type are depicted in Table C-2.
Table C-2. National Mean Building Type to Building Size Ratio for Census Tracts
Building Type

Ratio Type

Ratio

Multi-Family 2 to 4 Units

Small to Medium

68%

Multi-Family 5 to 19 Units

Small to Medium

60%

Multi-Family 20+ Units

Medium to Large

73%

C.2. Small Building Suitability Model
Gagnon et al. (2016) found that medium and large buildings are almost unanimously suitable,
because they are overwhelmingly flat with at least one 10m2 plane, and small buildings(<= 5,000 ft2) suitability was more variable. For these reasons, we apply a predictive modelingapproach for estimating small buildings’ suitability. For medium and large buildings, we assume
a percent suitability of 100%. The following describes the small building suitability model.
The small building suitability model is a multiple regression model for describing percent
suitability of small buildings (Gagnon et al. 2016). It relies on data from the U.S. National Land
Cover Database (NLCD) (MRLC 2011), the National Center for Education Statistics (NCES
2015), and the U.S. Census Bureau (U.S. Census 2010). Predictive variables include thenorthing, locale, census division, and percent land cover for water, developed open space,developed high-intensity, and all forest-type land uses. The model specification is shown in (1).
Percent_Suitable = Locale*Census_Region + Northing + Water + Forest +Developed_Open_Space + Developed_High_Intensity(1)

Though the model originally was applied to zip codes, it was designed to be applied using any
geographic area. This analysis utilizes this model and applies it to the Census Tract. As such, weused tract-specific data (e.g., assigning the locale of a tract based on the tract centroid rather thanthe centroid of the larger zip code area) and updated data vintages for both the NLCD and the
NCES.
The Analysis of variance (ANOVA) results for the predictive modeling are detailed in Table C3. We applied this model to 75% of the LiDAR-derived tracts and assessed the predictive
47
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accuracy using the remaining 25% of the LiDAR data. When comparing the predicted values tothe actual values, we find that 68% of the estimates are within 10% of the actual values and 90%of the predicted values are within 20% of actual values. We note that these numbers are 8% to
10%greater (68% versus 60% and 90% versus 80%) than the original zip code assessment(Gagnon et al. 2016). The increase in predictability is likely a consequence of geospatiallyresolved tract-level assessment, the increased number of tract observations, and the higher
resolutions of the newer vintage data.
Table C-3. ANOVA of Small Building Suitability OLS Model
Source

DF

SS

MS

F

P-Value

Locale Description

3

0.29

0.10

8.87

0.0000Census Division

8

23.93

2.99

272.410.0000Developed—Open Space

1

5.38

5.38

489.620.0000Developed—High Intensity

1

0.09

0.09

8.03

0.0046

Forest

1

0.67

0.67

60.96

0.0000

Water

1

0.07

0.07

6.50

0.0108

Northing

1

5.53

5.53

503.150.0000Locale Description * Census Division

24

3.71

0.15

14.09

0.000011,509130.43

0.01——

Residuals

C.3. Rooftop Plane Area Model
The third model in the series is a predictive model used to predict the number and size ofdevelopable planes on a building. This predictive model follows a statistical distribution fitting
approach. The number of developable surfaces is defined as the mean of a fitted exponential
distribution for each building size class (i.e., small, medium, large). The number of suitable
planes was calculated using a random sample from a national distribution of building suitable
plane counts, per building size class (Table C-4).

48
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Table C-4. National Distributions of Building Plane Count Probabilities by Building Size Class
Number of Planes

Small

Medium

Large

1

50%

21%

14%

2

28%

14%

7%

3

14%

13%

6%

4

5%

12%

5%

5

2%

10%

5%

6

1%

8%

5%

7

—

6%

5%

8

—

5%

4%

9

—

3%

4%

10

—

2%

4%

11

—

2%

3%

12

—

2%

3%

13

—

2%

3%

14

—

2%

3%

15–16

—

—

3%

17–20

—

—

2%

21–32

—

—

1%

Source: Gagnon et al. (2016).

Using a per building size class random sample, Gagnon et al. (2016) used the mean of the fitted
probability density function to calculate the size of each developable plane. Because buildings
with multiple planes were found to have a single plane larger than the remaining planes, the firstplane in a building’s sequence of multiple planes are fitted differently than the subsequent
planes. Additionally, in in their assessments Gagnon et al. (2016) found that buildings with only
one plane generally have a larger single plane. Therefore, to account for this, they built
exponential fits for one-plane buildings separately from multi-plane buildings. These distribution
fitting assessments were used here to inform the imputation of developable plane size. Table C-5
details the fitted areas assigned to each plane given the plane’s sequential index and thebuilding’s size class. Confidence intervals for each plane’s size were computed using the Gagnon
et al. (2016) methodology for the 95th percentile range.

49
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Table C-5. Fitted Areas for Each ith Plane, per Building Size Class
Building Size

ith Plane

Fitted Area

Small

1

33.81 m2

2–6

24.08 m2

1

800.00 m2

2-14

31.74 m2

1

4,000.00 m2

2-32

181.81 m2

MediumLarge

Source: Gagnon et al. (2016).

C.4. Rooftop Tilt and Azimuth Model
The rooftop tilt and azimuth model was developed by Gagnon et al. (2016) to predict the percentof rooftop planes with a particular tilt and azimuth class. This predictive model follows astatistical distribution fitting approach to predict the probability of plane having a particular tilt
and azimuth given the buildings size and the number of planes on the building. For smallbuildings, per locale–type national average values are used from a lognormal fit of data in eachof the azimuth-tilt classes. For medium and large buildings, we use national average values foreach azimuth-tilt class. Table C-6 shows the national average tilt and azimuth distribution forbuilding planes by building type used to sample a plane’s orientation.

50
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Table C-6. National Average Tilt/Azimuth Distribution for Building Planes, by Building Size Class
Building
Size

Tilt

Small

0˚

Azimuth
Flat

E

SE

S

SW

W

15˚

0.03

0.02

0.06

0.02

0.03

28˚

0.09

0.06

0.18

0.06

0.09

41˚

0.02

0.02

0.03

0.01

0.02

15˚

0.02

0.01

0.04

0.01

0.02

28˚

0.03

0.02

0.05

0.02

0.03

0.26

54˚
Medium

0˚

0.74

41˚

0.01

54˚
Large

0˚

0.93

15˚

0.01

0.01

0.01

28˚

0.01

0.01

0.01

41˚
54˚
Source: Gagnon et al. (2016).

51
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Appendix D. Quantifying the Technical Potential and
On-Site Consumption of LMI-Serving Buildings inThree Case Study Cities
We investigated the possible role for third-party buildings that serve low-income populations tohost solar on their roofs. The specific building classes investigated are: Public sites (e.g.,libraries, police stations, local government), Public housing (i.e., federally assisted rental
housing), K–12 public schools, Homeless shelters, and Places of Worship. These building classes
were chosen based on a combination of nonprofit status, a priori expectation of magnitude ofpotential, and feasibility in collecting GIS data. These building classes are not intended to becomprehensive of all third-party buildings that could deploy solar, and future analysis could
examine potential for multiple other building classes, including on for-profit commercial
buildings. Because of data limitations, estimates for these building classes are restricted to thecities of Chicago, Illinois; San Bernardino/Riverside, California; and Washington, D.C.

D.1. Quantifying the Technical Potential of Third-Party Buildings
To quantify the technical potential of LMI-serving buildings in the case study cities, we obtained
GIS data for each building type and spatially tagged these layers to our LiDAR buildings data.
This allowed us to identify the total technical potential for each building type in each city.
Described in this appendix are the data sources and methods used to spatially join varied GIS
layers of building locations to LiDAR building footprints.
The LMI-serving building data is an agglomerate data set sourced from a variety of locations,
including both open and proprietary sources. Table D-1 lists all the data used to identify LMIserving building footprints and then estimate the technical potential for each building class in ourcase study cities. Because the data consists of varied data sets and formats, we inevitably usedvaried methods to tag each building geometry to one or more LiDAR building footprints.
Discussed below are the case-by-case methods used for each building type.

52
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Table D-1. Core GIS Data Sources for LMI-serving Buildings in Case Study Cities
Building TypeCity

SourceK–12 Public
SchoolsAll

The National Center for Education Statistics’ (NCES) Core Common
Data (CCD), 2015–2016.

Homeless
Shelters

ChicagoChicago’s Department of Family and Support Services, HomelessServices Map.
Downloaded from the Chicago Data Portal, 2017.

Homeless
Shelters

Washington,
D.C.

DC Department of Human Services’, Homeless Shelter Locations.
Downloaded from Open Data DC, 2017.

Homeless
Shelters

San
Bernardino;Riverside

Google Maps, 2017.

Places ofWorshipAll

Homeland Security Infrastructure Program (HSIP) Gold, 2013.

PublicHousingAll

The National Housing Preservation Database (NHPD), 2016 Active
Properties.

Public Sites

Washington,
D.C.

2016 District Government Land (Owned, Operated, and or
managed), from DC Government.
Downloaded from Open Data DC.

Public Sites

Riverside

Riverside County, 2017.

Public Sites

ChicagoLibraries: Chicago Public Library
Police Stations: City of Chicago
Fire Stations: City of Chicago
Senior Centers: City of Chicago; Family and Support Services
Community Centers: City of Chicago; Family and Support Services
Workforce Centers: City of Chicago; Family and Support Services
County Government Administrative Facilities: Cook County
Department of Facilities Management
All data downloaded from the Chicago Data Portal, 2017 except forthe county administration data, which was downloaded from Cook
County Government Open Data, 2017.

53
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K–12 Public Schools
Two data sets were used to geotag public schools with LiDAR building footprints: (1) the
geocoded addresses from the National Center for Education Statistics’ Common Core of Data
(CCD), K12 Pubic data set (NCES CCD 2015-2016), and (2) campus boundaries fromOpenStreetMap (OSM). 26
Because each point in the CCD data set represents a single school’s address location, we used the
OSM school boundaries to help tag a single point to the set of buildings within the school
campus. In the cases where an OSM boundary did not exist, we tagged the CCD school point to
the nearest LiDAR building, using a maximum search radius of 500 meters. We ensured that no
double-counting was occurring in the cases where multiple schools share the same building(s).
Finally, technical potential was calculated from the aggregate of all school buildings, per city.
Homeless Shelters
Data for homeless shelters is an agglomerate data set. Where possible, we collected fromindividual city GIS repositories (i.e., Chicago and Washington, D.C.). For Chicago, we had tofilter the Homeless Services data layer to include only homeless shelters. Where theseauthoritative government data sets did not exist (i.e., San Bernardino and Riverside), wecollected coordinate data from Google Maps searches of homeless shelters in these cities. The
resulting data set is an agglomeration point layer of homeless-shelter locations. We then tagged
these homeless-shelter points to LiDAR buildings based on a nearest neighbor join, applying amaximum search radius of 500 meters. Finally, technical potential was calculated from theaggregate of homeless shelter buildings, per city.
Places of Worship
The places of worship layer was obtained from a single proprietary data source, the Homeland
Security Infrastructure Program (HSIP) Gold (HSIP Gold 2013). Using this layer, we tagged thepoint locations to the LiDAR buildings polygons using a nearest neighbor method, applying amaximum search radius of 500 meters. We ensured that no buildings were being double-counted
in the cases where more than one place of worship occupied the same building. Finally, technicalpotential was calculated from the aggregate of places of worship buildings, per city.
Public Housing
The public housing layer was obtained from a single data source, the 2016 Active Properties
from the National Housing Preservation Database (NHPD 2016). Because the NHPD data set
represents a single point location for multiple buildings that could span multiple parcels, we used
the ownership attribution and parcel data to identify all the buildings belonging to a single
NHPD property point. Parcel data was collected from individual government authorities (seeTable D-2) and partial fuzzy join methods 27 were used to join parcel ownership attributes to
NHPD property-owner attributes and identify all the parcels associated with each NHPD

26

School boundaries were identified from a 2017 OSM United States extract, where “school” was tagged in thebuilding and amenity keys.
27
We set the partial fuzzy match cutoff to 80%. This was determined to be a conservative cutoff resulting in only ahandful of false negatives and very few false positives.

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

property. We then tagged these parcels to the LiDAR building data to identify all buildings
associated with a NHPD property. Care was taken to ensure that no two properties overlapped,
and that double-counting was not occurring. The final technical potential was calculated from theaggregate of all public housing buildings, per city.
Table D-2. Parcel Data Used for Appropriating Buildings to Public Housing Properties
City

SourceChicagoParcel 2015, Cook County Clerk Map Department. Downloaded from Cook
County Government Open Data.

Riverside

Parcels layer from the Assessor’s County GIS geodatabase. Downloaded fromRiverside County Open Data, 2017.

San Bernardino

County of San Bernardino’s Geographic Information Services, Parcel Basemap.
Retrieved from FTP Services, 2017.

Washington, D.C.

Common Ownership Lots, from the District's Vector Property Mapping Database.
Downloaded from Open DC Data, 2017.

Importantly, Chicago’s parcel data layer did not obtain ownership attribution. As a result, wewere unable to tag NHPD points to all parcels associated with the property. Instead, we simply
tagged the NHPD property points to the single intersecting parcel. Therefore, we are likely
underestimating the total technical potential of public housing units in Chicago.
Public Sites
The public sites layer also is an agglomerate layer collected from a variety of sources. The layer
consists of public site locations for Chicago, Riverside, and Washington, D.C. For both
Washington D.C. and Riverside, we obtained the public sites data directly from the city or
county data repositories. For Chicago, we collected the data in a piecemeal fashion from the cityand county open data repositories. Filters were applied on these layers to include only sites
pertaining to government administration buildings, rec centers, senior centers, community
centers, workforce centers, libraries, police stations, and fire stations. For each public site, a
LiDAR building were tagged based on a nearest neighbor algorithm, applying a maximum searchradius of 500 meters. Care was taken to ensure no double counting of buildings in the cases
where two public sites occupy the same building. Finally, technical potential was calculated
based on the aggregate of the tagged buildings, per case study city.
We note two important qualifications regarding the public sites estimates. First, we were unable
to identify public sites technical potential for San Bernardino, California, due to data limitations.
Despite numerous requests, we were unable to obtain a public sites layer from the San
Bernardino County. Second, this method assumes one building per site, and therefore, estimatesof technical potential could be underestimated, especially for government administrativebuildings which likely have multiple buildings per site.

D.2. Estimating the On-Site Consumption of Third-Party Buildings
To estimate the on-site consumption of LMI-serving third-party buildings in our case study
cities, we used representative building loads from the 2009 Residential Energy Consumption
Survey (RECS) or the 2012 Commercial Building Energy Consumption Survey (CBECS) (EIA
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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2013; EIA 2016a). Each building type was tagged to the most representative building in RECS or
CBECS and was given the building consumption (kWh/year/ft2) of that building type per Census
Division. For each representative building, we calculated the average annual building
consumption per roof area (kWh/year/ft2), where roof areas were estimated from the number offloors and the total building area. Table D-3 describes the specific criteria used to tag eachbuilding type with its most appropriate building representative.
Table D-3. Representative Building Types Used for Third-Party Building Energy Consumption
Estimates
Building Type

Energy Consumption Survey

Query Applied

K–12 Public Schools

CBECS 2012

Filter for education buildings identified as
elementary, middle, or high schools.

Homeless Shelters

CBECS 2012

Filter for buildings with primary building
activity of dormitory.

Places of Worship

CBECS 2012

Filter for religious worship buildings
identified as places of worship.

Public Housing

RECS 2009

Filtered for buildings under the Public
Housing Authority.

Public Sites

CBECS 2012

Filtered for state or local governmentowned buildings where the primarybuilding activity is one of the following.
Administrative/professional office
Government officeFire station/police station
Other public order and safety
Library
Recreation
Social/meeting
Other public assembly
Courthouse/probation office

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.