NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Rooftop Solar 

Photovoltaic Technical 

Potential in the United States

Pieter Gagnon, Robert 

Margolis, Jennifer Melius, 

Caleb Phillips, Ryan Elmore

January 2016

NREL/PR-6A20-65586
2

Introduction

• Research question: 

o How much energy could be 

generated if we installed PV on all 

existing, suitable roof area in the 

United States?

• We answered this question 

through lidar-based GIS analysis, 

statistical modeling, and PV- generation simulations.

• Our results do not exclude 

systems based on their 

economic performance, and 

thus they provide an upper 

bound on potential deployment 

rather than a prediction of 

actual deployment. 

• The results are sensitive to 

assumptions about module 

performance, which is expected 

to continue improving over 

time. Furthermore, these 

estimates do not consider the 

immense potential of ground- mounted PV. 

Building Class 

(building footprint)

Total 

Suitable 

Area 

(billions 

of m2)

Installed 

Capacity 

Potential 

(GW)

Annual 

Generation 

Potential 

(TWh/year)

Annual 

Generation 

Potential (% of 

national sales)

Small (< 5,000 ft2) 4.92 731 926 25.0%

Medium (5,000–

25,000 ft2)

1.22 154 201 5.4%

Large (> 25,000 ft2) 1.99 232 305 8.2%

All Buildings 8.13 1,118 1,432 38.6%

Estimated Suitable Area and Rooftop PV Technical Potential by Building Class
3

Contents

• Introduction

• Processing Lidar Data to Estimate Rooftop Area 

Suitable for PV Deployment

• Analyzing PV Potential of Lidar-Covered Cities

• Modeled Estimates of Small-Building PV 

Technical Potential Nationwide

• Modeled Estimates of Medium- and Large- Building Technical Potential Nationwide

• Estimated National PV Technical Potential for All 

Buildings

• Conclusions
4

Input Lidar Data

• The lidar data used in our analysis were obtained from the U.S. 

Department of Homeland Security (DHS) Homeland Security 

Infrastructure Program for the years 2006–2014. 

• For each of the 128 cities in the data set, DHS provided lidar data in 

raster format at 1-m by 1-m resolution and a corresponding polygon 

shapefile of building footprints. 

Area Covered by Lidar Data

• The data set 

includes 26.9 

million buildings 

and 7.7 billion m2 of 

rooftop area, or 

about 23% of U.S. 

buildings. 

• The area covered 

represents about 

122 million people 

or 40% of the U.S. 

population. 
5

Excluding Unsuitable Roof Area

• We used the lidar data to 

determine how much roof area 

was available for PV deployment.

• Roof Area was considered 

unsuitable for PV if:

o The solar energy loss from shading 

was >20% of energy that would be 

received if unshaded.

o The roof tilt was greater than 60 

degrees.

o The roof azimuth faced northeast 

through northwest.

o The contiguous area of the roof 

plane was less than 10m2.

Example of Suitable Roof Planes in a 

Neighborhood within our Lidar Dataset
6

Observed Roof Plane Characteristics

• We subdivided all 26.9 million buildings into three 

classes according to the planar area of their footprints:

Building 

Class

Building 

Footprint (ft2)

Percentage of 

Building Stock

Percentage of 

Total Roof 

Area

Percentage of 

Buildings with at

Least One Suitable 

Roof Plane

Percentage of Roof 

Area Suitable for 

PV within each Size 

Class

Small < 5,000 94% 58% 83% 26%

Medium 5,000–25,000 5% 18% 99% 49%

Large > 25,000 1% 24% 99% 66%

All Buildings - 100% 100% 84% 32%

• Small buildings show substantially more variability in 

rooftop PV suitability than do medium and large buildings, 

because they have more diverse architectures (e.g., roof 

tilt) and more shadowing from trees and neighboring 

buildings. 

• Flat planes are very common on large buildings (93% of 

planes on large building are flat) and medium buildings 

(74%) but less common on small buildings (26%). 
7

Contents

• Introduction

• Processing Lidar Data to Estimate Rooftop Area 

Suitable for PV Deployment

• Analyzing PV Potential of Lidar-Covered Cities

• Modeled Estimates of Small-Building PV 

Technical Potential Nationwide

• Modeled Estimates of Medium- and Large- Building Technical Potential Nationwide

• Estimated National PV Technical Potential for All 

Buildings

• Conclusions
8

Simulating PV Productivity on Suitable Rooftop Area

• Energy generation of PV modules covering 

suitable roof area was simulated using NREL’s 

System Advisor Model (SAM).

• Technical assumptions reflect average 

performance of PV systems as they were installed 

in 2014, representing a mixture of 

monocrystalline-silicon, multicrystalline-silicon, 

and thin film modules, as opposed to universal 

installation of premium systems. 

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

0.70 0.98

Azimuth South facing Midpoint of azimuth class

Module power density 160 W/m2

Total system losses 14.08%

Inverter efficiency 96%

DC-to-AC ratio 1.2

Assumptions for PV Performance Simulations
9

Small Building Suitability

• This figure shows the percentage of 

small buildings that have a suitable 

roof plane at the ZIP code level. o Only small building suitability is 

mapped, because over 99% of 

medium and large buildings have at 

least one roof plane suitable for PV 

deployment. 

• There is only a weak trend of high 

building density driving down the 

suitability of small buildings. o Most of the highly developed 

downtown ZIP codes in these cities 

have suitability similar to the 

suitability in other ZIP codes within 

the city boundaries.

o However, some suburban ZIP codes 

outside city boundaries do show 

higher levels of suitability. 
10

Small Building Average Relative Production

• Average relative production of 

small buildings is defined here as 

the annual electricity generation 

potential for an average small 

building as a percentage of the 

average household annual 

electricity consumption in that 

city’s state (EIA 2009). 

• In many parts of the United States, 

ZIP-code-sized aggregations of 

households can collectively 

generate enough electricity to 

offset their expected annual 

consumption. 

Note: Because the consumption value is a state average, it is constant 

across all ZIP codes for a given city and therefore does not capture 

household-level variation in consumption that would be driven by 

socioeconomic status, building size, and other household-specific 

factors. Therefore, the average relative production values shown should 

only be interpreted as a simple estimation of the potential ability for a 

group of households in a given ZIP code to offset its consumption.
11

Technical Potential of All Buildings in 47 Cities Covered by Lidar

• The cities with the largest potential installed capacity are Los Angeles 

(9.0 GW) and New York (8.6 GW).

City Installed Capacity 

Potential (GW)

Annual Energy 

Generation Potential 

(GWh/year)

Ability of PV to 

meet Estimated 

Consumption

Worcester, MA 0.5 643 42%

Atlanta, GA 1.7 2,129 41%

New Orleans, LA 2.1 2,425 39%

Hartford, CT 0.4 404 38%

Baltimore, MD 2.0 2,549 38%

Bridgeport, CT 0.4 435 38%

Detroit, MI 2.6 2,910 38%

Portland, OR 2.6 2,811 38%

Milwaukee, WI 2.1 2,597 38%

Boise, ID 0.5 760 38%

Des Moines, IA 0.8 1,026 36%

Cincinnati, OH 1.0 1,176 35%

Norfolk, VA 0.8 1,047 35%

Wichita, KS 1.1 1,537 35%

Newark, NJ 0.6 764 33%

Philadelphia, PA 4.3 5,289 30%

Springfield, MA 0.3 370 29%

Chicago, IL 6.9 8,297 29%

St. Paul, MN 0.8 903 27%

Pittsburgh, PA 0.9 907 27%

Minneapolis, MN 1.0 1,246 26%

Charleston, SC 0.3 407 25%

New York, NY 8.6 10,742 18%

Washington, DC 1.3 1,660 16%

City Installed Capacity 

Potential (GW)

Annual Energy 

Generation 

Potential 

(GWh/year)

Ability of PV to 

meet Estimated 

Consumption

Mission Viejo, CA 0.4 587 88%

Concord, NH 0.2 194 72%

Sacramento, CA 1.5 2,293 71%

Buffalo, NY 1.2 1,399 68%

Columbus, GA 1.1 1,465 62%

Los Angeles, CA 9.0 13,782 60%

Tulsa, OK 2.6 3,590 59%

Tampa, FL 1.4 1,952 59%

Syracuse, NY 0.6 657 57%

Amarillo, TX 0.7 1,084 54%

Charlotte, NC 2.6 3,466 54%

Colorado Springs, CO 1.2 1,862 53%

Denver, CO 2.3 3,271 52%

Carson City, NV 0.2 386 51%

San Antonio, TX 6.2 8,665 51%

San Francisco, CA 1.8 2,684 50%

Little Rock, AR 0.8 1,099 47%

Miami, FL 1.4 1,959 46%

Birmingham, AL 0.9 1,187 46%

St. Louis, MO 1.5 1,922 45%

Cleveland, OH 1.7 1,881 44%

Toledo, OH 1.4 1,666 43%

Providence, RI 0.5 604 42%

Note: City boundaries defined by U.S. Census Bureau 2013 TIGER/Line Shapefiles. Roof area outside the city 

boundaries were not included in calculating the total capacity and energy estimates.
12

Contents

• Introduction

• Processing Lidar Data to Estimate Rooftop Area 

Suitable for PV Deployment

• Analyzing PV Potential of Lidar-Covered Cities

• Modeled Estimates of Small-Building PV 

Technical Potential Nationwide

• Modeled Estimates of Medium- and Large- Building Technical Potential Nationwide

• Estimated National PV Technical Potential for All 

Buildings

• Conclusions
13

Estimate of Small Building Suitability

• Previously we 

presented results for 

regions where we have 

lidar coverage. The 

remainder of this 

presentation shows 

estimates for larger 

regions where there is 

incomplete or no lidar

coverage.

• There are regional trends in small building suitability.

• The percentage of small buildings that are suitable tends to be 

higher in regions without significant tree canopy coverage.

o For example, the relatively unforested southeast portion of Washington has a 

higher percentage of suitability than the northeastern region of the state. 
14

Estimate of Small Building Productivity

• Broadly speaking, average small building production strongly 

correlates with the solar resource; however, there exists 

significant local variation driven by average household 

footprint and suitability. 

o For example, the simulated average production in Florida is 

12,100 kWh/year per small building (130% of the national 

average) owing to an above-average solar resource, but it 

ranges from 5,300 kWh/year to 30,100 kWh/year on a ZIP code 

level because of variation in suitability and building footprint.

• Differences in suitability can 

drive differences in total 

productivity between regions 

with similar solar resource. 

o For example, lower suitability in 

the south Atlantic states leads 

to lower average small building 

productivity compared to the 

Florida peninsula, despite a 

solar resource of similar quality.
15

Estimate of Small Building Average Relative Production

• Average relative production 

of small buildings at the state 

level: Defined as the annual 

rooftop PV generation of an 

average small building as a 

percentage of each state’s 

average annual household 

consumption.

o For example, the average 

generation potential of small 

buildings in Colorado is 8,760 

kWh/year, and the average 

annual household consumption 

is 7,370 kWh/year. Therefore 

the average relative production 

of Colorado’s small buildings is 

118%. 

• These results show that a relatively poor solar resource does not preclude a state’s 

residential sector from offsetting a significant percentage of its consumption. 

o An average small building across all of New England’s states, except Rhode Island, could generate 

greater than 90% of the electricity consumed by an average household in the region. This is driven by 

the low average household consumption of 8,011 kWh/year in the region (70% of the national 

average), which is due in part to high use of natural gas and oil for heating as well as relatively low 

summer cooling requirements.
16

Contents

• Introduction

• Processing Lidar Data to Estimate Rooftop Area 

Suitable for PV Deployment

• Analyzing PV Potential of Lidar-Covered Cities

• Modeled Estimates of Small-Building PV 

Technical Potential Nationwide

• Modeled Estimates of Medium- and Large- Building Technical Potential Nationwide

• Estimated National PV Technical Potential for All 

Buildings

• Conclusions
17

Estimated Energy Production for Medium and Large Buildings

• Across all states, small 

buildings have greater 

potential (926 

TWh/year, or 25% of 

total national 

electricity sales) than 

the combined 

potential of medium 

and large buildings 

(506 TWh/year, or 

14% of total national 

sales). 

• The differences in potential between building size classes can largely be explained by the total suitable roof 

area and the utilization of the available space. 

o Despite the higher percentage of medium and large building roof area suitable for PV deployment, the far smaller 

numbers of these buildings resulted in a lower total developable area (3.2 billion m2) compared to small buildings 

(4.9 billion m2). 

o The generally tilted roof area on small buildings can be used more efficiently than the predominantly flat roofs of 

large buildings, because flat roofs require greater spacing between modules to prevent excessive losses from shading. 
18

Contents

• Introduction

• Processing Lidar Data to Estimate Rooftop Area 

Suitable for PV Deployment

• Analyzing PV Potential of Lidar-Covered Cities

• Modeled Estimates of Small-Building PV 

Technical Potential Nationwide

• Modeled Estimates of Medium- and Large- Building Technical Potential Nationwide

• Estimated National PV Technical Potential for All 

Buildings

• Conclusions
19

Estimated Energy Production for All Buildings

The total nationwide technical potential of PV from buildings of 

all sizes 1,118 GW of installed capacity and 1,432 TWh of annual 

energy generation, which equates to 39% of total national 

electric sales. 
20

Technical Potential of All Buildings

• California has the 

greatest potential to 

offset use—PV on its 

rooftops could 

generate 74% of the 

electricity sold by its 

utilities in 2013. 

• A cluster of New 

England states could 

generate more than 

45%, despite these 

states’ below-average 

solar resource. 

• Washington, with the lowest population-weighted solar resource in the 

continental United States, could still generate 27%. 
21

• The six states with the highest potential PV generation as a percent of total state sales all have 

significantly below-average household consumption, suggesting the role an energy-efficient 

residential sector could play in achieving a high penetration of energy from rooftop PV.

State

Annual 

Generation 

Potential 

(% of sales)

Installed 

Capacity 

Potential 

(GW)

Annual 

Generation 

Potential 

(TWh/year)

Total Roof Area 

Suitable for PV 

Deployment 

(millions of m2)

California 74.2% 128.9 194.0 961

Maine 60.0% 6.3 7.1 45

Vermont 60.0% 3.0 3.4 21

Rhode Island 56.6% 3.8 4.4 28

New Hampshire 53.4% 5.3 5.9 38

Connecticut 49.8% 12.8 14.8 95

Massachusetts 47.0% 22.5 26.0 165

Florida 46.5% 76.2 103.2 557

Michigan 45.9% 42.1 47.3 303

Colorado 44.0% 16.2 23.5 119

Oklahoma 44.1% 19.3 26.4 140

New Mexico 43.4% 6.1 10.0 45

Missouri 42.7% 28.3 35.6 204

Kansas 41.7% 12.5 16.6 90

Nevada 39.6% 8.7 13.9 67

New Jersey 40.4% 24.9 30.1 184

Wisconsin 40.1% 23.6 27.7 169

Maryland 38.7% 19.3 23.9 142

Minnesota 38.5% 23.1 26.4 168

South Dakota 38.7% 3.8 4.7 26

New York 37.4% 46.6 55.3 340

Illinois 37.0% 44.1 52.5 324

Ohio 35.3% 46.8 53.0 338

Iowa 35.5% 14.0 16.6 99

Texas 34.6% 97.8 131.2 715

State

Annual 

Generation 

Potential 

(% of sales)

Installed 

Capacity 

Potential 

(GW)

Annual 

Generation 

Potential 

(TWh/year)

Total Roof Area 

Suitable for PV 

Deployment 

(millions of m2)

North Carolina 34.9% 35.0 45.3 252

Pennsylvania 34.5% 43.6 50.4 316

Nebraska 34.1% 8.2 10.5 60

Utah 34.3% 7.2 10.4 52

Oregon 34.2% 14.1 16.3 101

Georgia 33.8% 34.6 44.1 251

Arizona 34.4% 16.3 26.1 114

Arkansas 33.3% 12.2 15.5 88

Virginia 32.4% 28.5 35.8 205

Tennessee 31.9% 24.4 30.9 175

Mississippi 31.2% 11.7 15.2 84

Delaware 31.0% 2.9 3.5 20

Louisiana 29.8% 20.1 25.6 146

Alabama 29.8% 20.4 26.2 147

Indiana 29.5% 26.3 31.1 188

Montana 28.0% 3.2 3.9 21

Washington 26.6% 22.8 24.7 164

Idaho 26.4% 4.7 6.4 33

Kentucky 25.2% 18.0 21.4 131

South Carolina 25.5% 15.2 20.0 108

North Dakota 24.6% 3.3 3.9 23

West Virginia 22.9% 6.3 7.2 45

Washington DC 15.1% 1.3 1.7 11

Wyoming 14.2% 1.7 2.4 12

Contiguous U.S. Total 38.6% 1,118 1,432 8,130

Total Estimated Rooftop PV Technical Potential of All Buildings 

by State
22

Data for Public Use

• We have posted the supporting data on 

maps.nrel.gov/pv-rooftop-lidar. 

o Data layers include a regional summary and ZIP-code-level 

summary for all areas where lidar data are available as well as 

national coverage of ZIP code estimates as predicted by the 

methods described in this report. 

o Detailed documentation of each step in our analysis, including 

scripts for running the GIS tools, are linked to in the metadata 

section of each layer. This information can be accessed by 

clicking the question mark icon next to each layer in the table 

of contents. 

• This slide deck is a summary. For full report, see 

Gagnon, P., R. Margolis, J. Melius, C. Phillips, and R. 

Elmore. 2015. “Rooftop Solar Photovoltaic Technical 

Potential in the United States.” Golden, CO: National 

Renewable Energy Laboratory. NREL/TP-6A20-65298.