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Contract No. DE-AC36-08GO28308

Estimating Rooftop Suitability 

for PV: A Review of Methods, 

Patents, and Validation 

Techniques

J. Melius, R. Margolis, and S. Ong

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-60593

December 2013
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.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Estimating Rooftop Suitability 

for PV: A Review of Methods, 

Patents, and Validation 

Techniques

J. Melius, R. Margolis, and S. Ong

National Renewable Energy Laboratory

Prepared under Task No. SS13.1010

Technical Report

NREL/TP-6A20-60593

December 2013
NOTICE

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
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Acknowledgments

The authors would like to thank the following individuals for their input and comments on drafts

of this report: Easan Drury, Donna Heimiller, Keith Searight, Paul Denholm, Ryan Elmore

(NREL), J. Alstan Jakubiec (MIT), and David Levine (Geostellar). We would also like to thank 

the U.S. Department of Homeland Security, Trinity Solar, Namaste Solar, and the California 

Public Utilities Commission for the use of their data in support of our research and analysis. In 

addition, we would like to thank Jarett Zuboy for his editorial support and input. This work was 

supported by the U.S. Department of Energy under contract number DE-AC36-08GO28308.
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Table of Contents

Introduction ................................................................................................................................................. 1

1 Literature Review.................................................................................................................................. 3

1.1 Constant-Value Methods ................................................................................................................. 3

1.2 Manual Selection Methods .............................................................................................................. 6

1.3 GIS-based Methods.......................................................................................................................... 9

2 Patent Search...................................................................................................................................... 14

3 Validation of Results .......................................................................................................................... 16

4 NREL Method ...................................................................................................................................... 18

5 Validation of NREL Method ............................................................................................................... 21

6 Conclusions ........................................................................................................................................ 26

References................................................................................................................................................. 27

List of Tables

Table 1. Advantages and Disadvantages of Three Major Rooftop-area Estimation Methods ............ 1

Table 2. Range of Rooftop PV Availability Found in Reviewed Literature............................................ 2

Table 3. Findings of Studies about Constant-value Methods ................................................................ 3

Table 4. Findings of Studies about Manual Selection Methods............................................................. 6

Table 5. Findings of Studies about GIS-based Methods......................................................................... 9

Table 6. GIS Software Used in Reviewed Literature.............................................................................. 10

Table 7. Results of Patent Search ........................................................................................................... 14

Table 8. Advantages and Disadvantages of Validation Methods Used in Reviewed Literature ....... 16

Table 9. Assumptions in Reviewed Literature and NREL Method ....................................................... 18

Table 10. Results of NREL Validation ..................................................................................................... 24

List of Figures

Figure 1. NREL method for processing LiDAR data.............................................................................. 19

Figure 2. Cities included in NREL validation.......................................................................................... 21

Figure 3. Distribution of difference in slope........................................................................................... 25

Figure 4. Distribution of difference in size ............................................................................................. 25
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Introduction

A common question throughout solar energy research is: What is the technical potential of solar 

energy, given the total available land and rooftop area? For researchers looking to understand the 

market potential of rooftop-installed photovoltaics (PV) in particular, understanding the amount 

and characteristics of rooftop space that is available for installing PV is essential. Many methods 

of estimating rooftop area have been developed, ranging from simple multipliers of total building 

space to methods that employ complex geographic information systems (GIS) or threedimensional (3-D) models. This report reviews the literature and presents an alternative method 

for estimating rooftop suitability for PV that was developed by the National Renewable Energy 

Laboratory (NREL). 

This report reviews the literature on rooftop-area estimation to show how others have attempted 

to capture this difficult measurement. It reviews 35 studies and 6 patents. Many of the studies 

also estimate potential generation from PV systems; while this report describes those methods, it

focuses on the rooftop-area estimation methods.

Table 1 presents the advantages and disadvantages of the three major rooftop-area estimation

methods reviewed in the first three sections of this report: constant-value (Section 1.1), manual 

selection (Section 1.2), and GIS-based methods (Section 1.3).

Table 1. Advantages and Disadvantages of Three Major Rooftop-area Estimation Methods

Advantages Disadvantages

Constant-value 

Methods

This method is quick and rooftop area is 

easy to compute.

Generalized results do not consider 

localized rooftop characteristics.

Results are difficult to validate.

Manual selection 

methods

These methods are detail-specific and 

enable assumptions based on specific 

knowledge of regions and buildings.

Manual selection methods are timeintensive and not easily replicable across 

multiple regions.

GIS-based 

methods

These methods are detail-specific and 

replicable across multiple regions. They 

can be automated for less hands-on 

computing.

GIS-based methods are time-intensive

and computer-resource intensive.

Table 2 summarizes the wide range of metrics and results for each method found in the literature. 

These variations suggest that the literature cannot be used alone to validate estimates of rooftop 

PV availability for specific areas; rather, area- and project-specific validation methods are 

required. Section 2 summarizes the existing patents for similar methods of determining suitable 

areas for PV. Section 3 reviews the studies that validate their results. Sections 4 and 5 outline 

NREL’s estimation method and validation of that method. Section 6 provides a discussion and 

conclusions.
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Table 2. Range of Rooftop PV Availability Found in Reviewed Literature

Method Type

Number 

of 

Studies 

Reviewed

Percent of 

Total Rooftop 

Area Suitable 

for PV

Percent of 

Total 

Buildings

Suitable for 

PV

PV-suitable 

Rooftop 

area per 

Capita

Potential Percent 

of Energy 

Demand/

Consumption

from PV

Constant-value 

methods 11 15%–65% N/A 13.4–70 m2 10%–100%

Manual selection 

methods 9 1.31%–55% N/A N/A N/A

GIS-based 

methods 15 6.5%–59% 49%–50% N/A 5%–45%
3

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1 Literature Review

1.1 Constant-Value Methods

Constant-value methods of estimating rooftop availability are popular due to their ease of use; 

they are not time- or resource-intensive, and they provides a useful starting point for potential 

rooftop solar energy generation in a region. Many of the constant-value methods of rooftop-area

estimation consider typical rooftop configurations and estimate a multiplier that can be applied to 

an entire region. Most of these studies make rule-of-thumb assumptions about the proportion of 

sloped versus flat roofs, the number of buildings with desirable rooftop orientations, and the 

amount of space obstructed by building components such as heating, ventilation, and airconditioning (HVAC) systems and shadows. A variation of the constant-value method involves 

estimating available rooftop space based on the population density of a region. Table 3

summarizes the constant-value findings for studies that include at least some of the metrics found 

in the literature (see Table 2).

Table 3. Findings of Studies about Constant-value Methods

Study Study Area

Percent of 

Total Rooftop 

Area Suitable 

for PV 

PV-suitable 

rooftop

area per 

capita

Potential percent of 

energy demand/

consumption met by 

rooftop solar

Chaudhari et al. 2004, 

Denholm and Margolis 

2008, Frantzis et al. 

2007, Paidipati et al. 

2008

United States

60%–65%

(commercial)

22%–27%

(residential)

— —

Frantzis et al. 1998 Minneapolis/St. 

Paul metro area

35%–65% (flat)

16% (pitched) —

Up to 23% (total 

demand); up to 50% 

(daytime peak)

Ladner-Garcia and 

O'Neill-Carrillo 2009 Puerto Rico 50% —

All residential energy 

from 25% of residential 

rooftop space available;

all commercial and 

industrial energy from all 

available rooftop space

Vardimon 2011 Israel

30% (all 

buildings)

50% (large 

buildings only)

—

32% of electricity 

consumption (all 

buildings); 10%–15%

(large buildings only)

Eiffert 2003, IEA 2001 23 IEA countries

15% (Japan)–

57.8% (United 

States)

— —

Lehmann and Peter 

undated

NorthrhineWestfalia, 

Germany

—

13.4 m2

(rooftop)

7.1 m2

(façade)

—

Wiginton et al. 2010 Southeastern 

Ontario 30% 70 m2 30% of electricity demand
4

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A series of reports from Navigant and NREL (Chaudhari et al. 2004, Denholm and Margolis 

2008, Frantzis et al. 2007, Paidipati et al. 2008) use floor-space data provided by McGraw-Hill 

to extrapolate total rooftop area from total floor area. Weather-station data from typical 

meteorological year (TMY2 or TMY3) stations are also used to estimate solar resource 

throughout the United States. Major assumptions are made about the mix of the overall building 

stock, estimating that 8% of all U.S. residential rooftops are flat and that 63% of all commercial 

rooftops are flat. Commercial rooftop space is reduced to account for shading effects, and 

residential rooftop space is reduced to account for shading, slope, and orientation. These studies 

conclude that 60% to 65% of commercial rooftop space and 22% to 27% of residential rooftop 

space is suitable for PV, depending on whether the climate is warm or cool. 

Many other studies also make general assumptions regarding the proportion of pitched and flat 

roofs and estimate the total available rooftop space based on these building characteristics. 

Frantzis et al. (1998) identify buildings in the Minneapolis-St. Paul metropolitan area. They 

assume that 5% of flat-roofed buildings are covered by HVAC equipment or other building 

components that cause shadowing over 35% of the entire rooftop. They also assume that 30% of 

pitched roofs have an ideal orientation of 135 to 270 degrees. Applying these constants to the 

available building stock, determined from the 1990 Census and accounting for assumed shading 

from trees and building components, they conclude that 35% to 65% of flat roofs and 16% of 

pitched (residential) roofs are available for PV. They estimate that, with that amount of available 

rooftop space, up to 23% of total electric sales and up to 50% of daytime peak loads could be 

generated with rooftop solar power. 

Other research uses an even more general estimating approach, applying a constant value to the 

entire building stock, regardless of slope or orientation. Ladner-Garcia and O'Neill-Carrillo 

(2009) apply a constant value to total building area to estimate the PV energy potential for Puerto 

Rico. The total number of buildings is retrieved from the U.S. Census and rooftop area calculated

assuming a typical square footage for all residential, commercial, and industrial buildings. Half 

of all rooftop area is deemed suitable for PV to account for shadows and uncertainty in rooftop 

layout, and rooftop area is assumed to be distributed throughout Puerto Rico in proportion to the 

population. Using solar resource data for Puerto Rico, temperature and weather data for Puerto 

Rico, and PV panel specifications, the researchers estimate that all of the region’s residential 

energy needs could be supplied using 25% of the total residential rooftop space available, and 

that all of the commercial and industrial energy needs could be supplied with all available 

commercial and industrial rooftop space.

Vardimon (2011) estimates rooftop area in Israel using building data and shapefiles from the 

Israeli Central Bureau of Statistics. The buildings are classified by type (commercial, industrial, 

residential) and size, and a constant value of 30% is applied to the entire building stock as an 

assumed availability factor for rooftop area. The constant value is determined using 

considerations from other published studies included in this report as well as discussions with 

local solar installers. This “Total Potential Scenario” yields an estimated energy production 

potential equivalent to 32% of Israel’s electricity consumption. An alternative “Economic 

Scenario” is run using only buildings with rooftops that are larger than 800 square meters and 

have an assumed suitable rooftop area of 50%. Under this scenario, rooftop PV could provide an

estimated 10% to 15% of the nation’s electricity production.
5

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Another common method of determining a constant value for rooftop-area estimation is based on 

a region’s population density. Such studies apply a generic value to the entire building 

population to identify rooftop area suitable for PV but use a population-density formula to 

generate a more geographically specific value.

Reports from NREL (Eiffert 2003) and the International Energy Agency (IEA 2001) estimate 

rooftop availability for all 23 IEA countries. They evaluate buildings based on architectural and

solar suitability. Architectural suitability includes characteristics such as building components 

(e.g., HVAC, elevators), historical considerations, and shade. Solar irradiation values that 

account for slope and orientation are considerations for solar suitability. Variables used in the 

calculation include building area per capita, population size, solar yield, solar irradiation, and 

global conversion efficiency; a constant utilization factor of 0.4 is applied. Each country is 

assigned a constant rooftop-availability value based on population density, which factors in each 

of the other components. Eiffert (2003) and IEA (2001) do not discuss how the utilization factor, 

a value that describes the suitability of rooftops relative to total ground floor area, is derived. The 

reports conclude that 57.8% of U.S. rooftop space has suitable architecture and solar exposure

for PV. Other countries range from 15% (Japan) to 48% (Spain). These constant values are

calculated by sampling a small section of buildings in each country and applying those values to 

the total building stock.

In a study of the European Union, Lehmann and Peter (undated) use a data set of buildings in

Northrhine-Westfalia, Germany, with an assumed 13.4 square meters per capita of rooftop area 

and 7.1 square meters per capita of façade area suitable for PV. Correlation curves between 

rooftop area and population density are applied across all European Union countries to estimate

potential solar energy generation. A similar analysis for southeastern Ontario (Wiginton et al. 

2010) uses building footprints from a sample of 10 census areas to estimate rooftop availability. 

The building footprints are from Queens University, for neighborhoods with existing GIS data

sets, and they are extracted from aerial imagery. Combining the building footprints with census 

data, the researchers estimate that about 70 square meters per capita of rooftop space are suitable 

for PV. For the sampled areas, this equates to 30% of the total rooftop area. Applying the 

constant value of 30% to the rest of the study area—covering 48,000 square kilometers and 1.9 

million people in southeastern Ontario—it is estimated PV could generate up to 6,909 gigawatthours (GWh) of energy per year and supply about 30% of Ontario’s annual electricity demand. 
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1.2 Manual Selection Methods

Manually selecting rooftops from sources such as aerial photography represents a much more 

refined—albeit more time-intensive—method of identifying suitable rooftop space than constantvalue methods. Table 4 summarizes the findings of studies about manual selection that include at 

least some of the metrics found in the literature (see Table 2).

Table 4. Findings of Studies about Manual Selection Methods

Study Study Area

Percent of Total 

Rooftop Area Suitable 

for PV

Potential Percent of Energy 

Demand/Consumption Met 

by Rooftop Solar

Johnson and 

Armanino 2004

Marin County, 

California — Up to 30 megawatts (MW) of 

capacity

Armanino and 

Johnson undated

Phoenix and 

Scottsdale, Arizona 1.31%–11.6% —

Nguyen and Pearce 

2012, 2013 Kingston, Ontario 33% —

Root and Perez 

2006

Parking lots in 19 

New York counties — 2,947 MW of capacity

Zhang et al. 2009 Newark, Delaware — —

Bright and Burman 

2010 Portland, Oregon

3% of total district area

(not only rooftop area) 30%

Ordonez et al. 2010 Andalusia, Spain 51%–55% (flat)

16%–21% (pitched) —

The Community Development Agency in the County of Marin, California, conducted two studies

to estimate rooftop suitability; one was in the County of Marin (Johnson and Armanino 2004)

and one was in Phoenix and Scottsdale, Arizona (Armanino and Johnson undated). In both 

studies, rooftops are manually identified from aerial imagery based on their solar resource, land 

use, and location away from large HVAC units or other objects that could create large shadows. 

The footprints for these buildings are digitized and their rooftop areas are calculated. The total 

area is reduced by 25% to account for loss of PV performance due to panel spacing and other 

potential losses. In the County of Marin study, the analysts conclude that 7.7 million square feet 

of commercial rooftop area, 1.8 million square feet of institutional facilities rooftop area, and 3.4 

million square feet of parking lot area along Highway 101 through the county could be used for 

PV and could provide up to 30 megawatts (MW) of electricity-generation potential. In the 

Arizona study, the analysts conclude that rooftop area available for PV ranges from 1.31% to 

11.6% of total rooftop area. 

Analyses in Vienna, Austria (Wittmann and Bajons 1997), and Kingston, Ontario (Nguyen and 

Pearce 2012, 2013) use a similar approach. These studies select rooftops from aerial imagery 

with characteristics that appear suitable for rooftop PV (e.g., flat and south-facing roofs) and 

which appear to have no significant shade from building components or nearby trees and 

buildings. The Vienna study classifies buildings by slope, with 0 to 15 degrees being assumed 
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flat and two additional classes with slopes ranging from 15 to 45 degrees and 45 to 75 degrees. 

Slope data are taken from aerial photography using estimated heights. The classified slope data 

are combined with local solar resource data from nearby meteorological stations. No results are

presented in this study, and the analysts only mention shading criteria as a factor for future study. 

The Kingston studies (Nguyen and Pearce 2012, 2013) apply constant values to the selected 

rooftops, ranging from 50% to 62.5% for pitched roofs and 100% for flat roofs. Factoring in 

ratios of flat and pitched roofs and the space that is unobstructed by shading as found in the 

manual building-extraction processes, the researchers estimate that approximately 33% of total 

rooftop space is available for PV. The 2013 Kingston study goes further by using GIS tools to 

model sun positions on rooftops. This follow-up analysis is discussed in detail in Section 1.3.

A study specifically targeting parking lot areas in New York State (Root and Perez 2006) also 

identifies suitable area using aerial imagery. Root and Perez place parking lots in categories 

ranging from “no obstruction” to “major obstruction” based on the amount of shade covering 

each lot. The area that is suitable for PV within a parking lot is calculated based on these 

categories. Counties range in solar energy potential from a 9.6 MW to 552 MW, with a total 

2,947 MW of potential for the entire 19-county study region.

Many studies use online tools such as Google Earth and NREL’s IMBY (In My Backyard) to 

select suitable rooftops. For example, analyses of Newark, Delaware (Zhang et al. 2009), and 

Portland, Oregon (Bright and Burman 2010), use Google Earth to identify suitable rooftops. The 

Newark analysis selects only roofs with south-, southeast-, and southwest-facing planes. 

Rooftops are also visually inspected for shading and building obstructions, and buildings with 

architectural considerations such as historical designations are excluded. Walking surveys in 

some areas of the study validate the Google Earth selections. Rooftops are digitized from the 

aerial imagery and sorted according to residential, commercial, industrial, and university 

designations. The total potential PV generation is calculated using PV Planner software, a 

program developed by the Center for Energy and Environmental Policy that uses TMY data to 

simulate PV system performance. The analysis concludes that PV generation could supply over 

75% of Newark’s daylight-hour energy needs and over 30% of energy needs across all hours. 

The Portland analysis (Bright and Burman 2010) considers rooftops, awnings, carports, and 

parking garages for siting solar water heaters and PV. In this study, suitable areas must 

encompass more than 100 square feet and have an annual solar access of 97% or greater. The 

total suitable rooftop area is decreased by 10% to account for unforeseen obstructions. Five 

regions in Portland are considered. Within each region, a small sample of buildings is measured 

in Google Earth, and the percentage of suitable rooftop area is applied to the entire region. 

Measurements are verified in person for a small number of buildings using the sun-eye system, 

which estimates annual shade coverage. The study concludes that about 3% of total district 

area—not just rooftop area—is usable area for solar technology.

Google Earth is also used, along with local construction data, in an analysis for Andalusia, Spain

(Ordonez et al. 2010). This analysis uses sampling methods and manual rooftop identification to 

estimate the availability of residential rooftop space for PV. The analysts sample buildings by 

type, including detached/semi-detached houses, townhouses/row houses, and high-rise buildings. 

The footprint for each sampled building is manually digitized from Google Earth, and a 3-D 
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model is created in AutoCAD. Using the 3-D model, areas obstructed by HVAC equipment, 

antennas, chimneys, and other objects are excluded from the total rooftop area. Rooftops are

categorized by flat and pitched roofs, and a 1-meter perimeter is assumed necessary around all 

installations to account for maintenance work. The sampled buildings suggest that 51% to 55% 

of flat roof surface area and 16% to 21% of pitched roof surface area could be used for PV. 

The IMBY1 web application allows users to view aerial imagery and draw a PV system on any 

building to calculate potential energy output, costs, and savings. Anderson et al. (2010) use 

IMBY to estimate the suitable rooftop area for a sample of buildings in New York City, and they 

apply the resulting percentage to the study area of 10 utility-area networks in the city. The 

available rooftop space is used in calculations to determine total PV energy potential in 

the network.

 1 IMBY was integrated into PVWatts in 2013 and is available at pvwattsbeta.nrel.gov
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1.3 GIS-based Methods

The majority of rooftop analyses use GIS-based methods for estimating the suitable space for 

rooftop PV. The key distinction between these methods and the previously discussed methods is 

that decisions about rooftop suitability are not made using predetermined constant values or by 

manually selecting buildings. Instead, ideal values for rooftop characteristics are input into a 

computer model, and the GIS software determines areas of high suitability. This often results in a 

quicker, more objective, and more accurate method for identifying rooftop availability.

GIS-based methods use primarily 3-D models to determine solar resource or shadow effects on 

buildings. The 3-D models are most often generated from orthophotography or light detection 

and ranging (LiDAR) data, and they are combined with slope, orientation, and building structure 

data to estimate total solar energy generation potential. As LiDAR data has become more widely 

available at higher resolutions in recent years, this has become a much more desirable method for 

estimating rooftop area. Table 5 summarizes the findings for GIS-based studies that include at 

least some of the metrics found in the literature (see Table 2). Table 6 lists the GIS software used 

in the literature reviewed in this report.

Table 5. Findings of Studies about GIS-based Methods

Study Study Area

Percent of Total 

Rooftop Area 

Suitable for PV

Percent of Total 

Buildings 

Suitable for PV

Potential Percent of 

Energy 

Demand/Consumption 

Met by Rooftop Solar

Jofierka and 

Kanuk 2009

Bardejov, 

Slovakia

35% (residential)

59% (total) — 45%

Compagnon 

2004

Fribourg,

Switzerland 6.5%–21% — —

Kodysh and 

Omitaomu 

undated

University of 

Toledo, Ohio — — 5%

Anders and 

Bialek undated

San Diego 

County, 

California

12.6% of total 

developed land 50% —

Santos et al. 

2011

Lisbon, 

Portugal — 49% —
10

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Table 6. GIS Software Used in Reviewed Literature 

Content Website

ArcGIS Spatial Analyst 

(Hillshade and Area Solar Radiation tools) www.esri.com/software/arcgis/extensions/spatialanalyst

Autodesk Ecotect usa.autodesk.com/ecotect-analysis

Google Earth www.google.com/earth

Google SketchUp sketchup.com

GRASS r.sun grass.osgeo.org/grass64/manuals/r.sun.html

PVGIS re.jrc.ec.europa.eu/pvgis

RADIANCE/DAYSIM daysim.ning.com

Studies in Bardejov, Slovakia (Jofierka and Kanuk 2009), and Chandler, Arizona (Jo and 

Otanicar 2011), use orthophotography to create a 3-D model and estimate solar resource. The 

Bardejov study processes the 3-D building model in r.sun, a GRASS solar radiation tool similar 

to the ArcGIS Hillshade tool, to create a solar radiation map of the city. The r.sun tool returns the 

estimated areas of shadow and strength of solar radiation. The solar radiation map is used in 

coordination with PVGIS, a program designed specifically for Europe and Africa to estimate 

energy potential from a 1-kilowatt-peak system using GIS data, to estimate the total potential 

energy output from PV. The study concludes that 25 GWh of PV electricity could be produced 

annually, which is 45% of the current electricity consumption of the region. The study also 

concludes that approximately 35% of residential rooftop space and 59% of total rooftop space 

could be used for PV systems.

The Chandler study (Jo and Otanicar 2011) assesses imagery for brightness value to determine 

the potential areas of shadow and obstructions on commercial and government rooftops in a 4-

square mile area of the city. The lengths of shadows are estimated in detail using Google 

SketchUp by casting simulated shadows on a 3-D building model. The obstructions identified 

during the satellite imagery processing are buffered by a distance equal to the shadow lengths 

estimated in SketchUp. These areas are subtracted from the total rooftop area to estimate the area 

suitable for PV. The researchers visited 150 of the 932 buildings included in the study to take 

ground-truth measurements and validate the accuracy of their model. 

In the follow-up analysis to the original Kingston, Ontario, study (Nguyen and Pearce 2012) 

mentioned in Section 1.2, analysts ran a sample of 100 buildings through the GRASS r.sun tool 

(Nguyen and Pearce 2013). Rooftops with a southeast- through southwest-facing aspect (90 to 

270 degrees) and a slope within 15 degrees of the local latitude are considered suitable for PV. 

The results are validated on one rooftop in Kingston, where the shadows were measured during a 

morning hour and an evening hour to compare to the modeled results.

RADIANCE/DAYSIM, a program designed to simulate lighting scenarios, is used in an analysis 

in Fribourg, Switzerland (Compagnon 2004), and an analysis for Cambridge, Massachusetts,

conducted by the Massachusetts Institute of Technology (MIT; Jakubiec and Reinhart 2013). The 

Fribourg analysis uses a 3-D building model for 61 buildings in the study area. The methods for 

developing the 3-D building model are not discussed. The building model is run through 
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RADIANCE/DAYSIM, and researchers place a minimum of 1,000 kilowatt-hours per square 

meter (kWh/m2) on the model to determine suitable rooftop areas. The study concludes that the 

potential for PV systems ranges from 6.5% to 21% of total roof area. The researchers identify the 

need for validating the results of the study, which is not included in the scope of this project. The 

Cambridge analysis also uses the RADIANCE/DAYSIM model to create hourly irradiance maps 

for the city using the industry-standard Perez all-weather sky model in conjunction with TMY3 

data from Boston Logan International Airport. Rooftop area is considered to be suitable for PV if 

it has a slope less than or equal to 60 degrees and a solar resource value of at least 609 kWh/m2

. 

Results of this analysis are presented on a building-specific level and are accessible through an 

interactive Web interface.

Studies in Vancouver, British Columbia (Tooke et al. 2011), and Toledo, Ohio (Kodysh and 

Omitaomu undated) use the ArcGIS Solar Analyst tool to model the solar resource available on 

rooftops. LiDAR data are used in the Vancouver analysis to extract building footprints and trees. 

The solar radiation is estimated using Solar Analyst along with regional weather data obtained 

from the University of Oregon. The study concludes that solar radiation on rooftops varies from 

hourly and seasonally, and that trees reduce solar radiation on rooftops by an average of 38%. No 

additional rooftop analysis is included in this analysis. In the Toledo study, students worked with 

researchers at Oak Ridge National Laboratory to present a method for estimating the potential for 

solar energy production on the University of Toledo campus. LiDAR data for the University of 

Toledo campus is processed to a 1-meter-resolution digital surface model (DSM). The DSM is

run through Solar Analyst to produce a solar insolation map. The values from the solar insolation 

map are aggregated to conclude that 13,400,000 kWh could be produced annually on the 

campus, generating 5% of its total energy needs.

Another study to use the proportion of flat roofs versus pitched roofs is an analysis for San Diego 

(Anders and Bialek undated). In this analysis, buildings data from the City of San Diego are used 

to digitize all building footprints greater than 3,000 square feet. These buildings are run through 

ArcGIS Solar Analyst to determine the total solar resource available for large-area buildings. 

Rooftop area is manually assigned to one of three classifications: 20% suitable area, 60% 

suitable area, or 80% suitable area. The proportions of each class are extrapolated to all 

commercial buildings in the County of San Diego. It is assumed that 50% of all residential 

buildings would have space available for PV. Once total rooftop area is estimated, the area is

reduced by 20% to account for shading. Using these constant values, it is estimated that 12.6% of 

total developed land (as opposed to total buildings) is suitable rooftop space for PV in the 

County of San Diego.

A study of Lisbon, Portugal (Santos et al. 2011), uses LiDAR data to create a DSM that is then 

used as an input in the ArcGIS Area Solar Radiation tool to create a solar surface map. A solar 

radiation map is created for each month of the year, and the 12 values for each pixel are averaged 

to determine a final solar radiation value. Rooftop areas are considered suitable for PV if at least 

10 contiguous square meters have more than 1.68 megawatt-hours per square meter (MWh/m2

)

of solar radiation and have a slope of less than 45 degrees. The researchers determine that 49% 

of buildings in the approximately 6-square-kilometer study area in Lisbon have rooftops suitable 

for PV. 
12

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LiDAR data are used in a rooftop analysis of Toledo, Ohio, to model solar irradiation and 

shadows on roofs (Chanikarn and Mojtaba 2010). Calculations are performed on the LiDAR data 

to estimate building and tree height by subtracting the first return layer from the bare earth layer. 

Satellite imagery is also used to verify the locations of buildings and trees. Using Autodesk 

Ecotect 5.6, a building design software used to analyze and simulate building energy 

performance, the researchers estimate shadows on rooftops from building components and trees. 

This analysis provides no specific results or assumptions about what threshold of shadows or

rooftop size are considered usable space for PV.

Many studies use LiDAR point data to model solar resource, slope, and aspect in order to 

estimate suitable rooftop area and potential energy generation. Latif et al. (2012) use LiDAR data 

to create a DSM for Georgetown, Malaysia. From this DSM, slope, aspect, and solar radiation 

are calculated. The thresholds for slope include one scenario with a 15-degree maximum slope 

and a second scenario with a 30-degree maximum slope. Only rooftops with south-, southwest-, 

or southeast-facing aspects are considered. Additionally, rooftops identified as suitable for PV 

are required to have at least 1,000 watts per square meter and be at least 2.6 square meters in 

size, accounting for a 1.5 square meter PV panel size as well as 15% buffer space for the 

mounting frame and panel spacing. All criteria are converted to a binary value of 1 or 0 to 

determine suitability. The final step in the analysis, called the “human mask” layer, includes a 

visual inspection of all potentially suitable rooftops in Google Earth, and a manual decision to 

include the rooftop in the analysis is made based on the aerial imagery. Validation of the 

computer model and visual inspection is very limited, including only three buildings inspected in 

person to validate measurements.

In an analysis of Lujazui, Shanghai, China (Huang et al. 2012), LiDAR points are used to extract 

building footprints and create a solar radiation layer of the region. Individual roof planes are

identified for each building through a manual process using Google Earth. For each roof plane, a 

single point is identified and assigned a slope and aspect value. Each point is run through a 

neighborhood analysis in GIS to confirm that all points within a single plane are of the same 

slope and aspect. Only rooftop planes that are larger than 5 square meters and have a slope of 

less than 35 degrees; a south-, southwest-, or southeast-facing aspect; and at least 5 megajoules 

per square meter of solar radiation per day are considered suitable rooftops. The slope, aspect, 

area, and solar radiation attributes are summed, and the results are collected in a final list of 

suitable rooftops, although no results are presented in Huang et al. (2012).

LiDAR data are used in a detailed rooftop analysis of a 1-square-kilometer area of Maribor, 

Slovenia (Lukac et al. 2013), to create a digital elevation model (DEM) and a solar radiance map 

as well as to calculate the slope and aspect of each roof plane. Additionally, lower-resolution 

DEMs are used to identify areas of shadow cast by larger regional influences such as nearby hills 

or mountains. Roof planes are identified using a seed process to group similar cells based on a 

neighbor analysis. The thresholds for slope and aspect suitability are not discussed. Additional 

filters considering the cultural or protected status of buildings and the presence of rooftop 

components such as HVAC equipment and chimneys are also applied, but Lukac et al. do not 

specify whether this step is done manually or in a GIS. The result of this analysis is a data set of 

five rankings for PV, ranging from most suitable to unsuitable. 
13

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Two California-based analyses (Levinson et al. 2008, 2009) gauge the impact of tree growth over 

time on rooftops by evaluating residential, tree-dense sample areas in Sacramento, San Jose, Los 

Angeles, and San Diego in which homes were built in the 1980s; each sample area covers 2 to 4

square kilometers. The ArcGIS Hillshade tool is used on LiDAR data to estimate areas of 

shadow on rooftops for each hour of the 21st day of each month, resulting in 143 shade 

simulations per year. If the area of the rooftop is in shadow, it is given a value of 1. The 

researchers conclude that, over the course of approximately 30 years, rooftop space free from 

shadows decreased by 50% to 70% due to tree growth. This issue has substantial impacts on 

rooftop availability estimates in newer neighborhoods, and we have identified it as an area for 

future research in connection with NREL’s work. Levinson et al. (2009) focuses on San Jose and 

is a more in-depth explanation of Levinson et al. (2008). Using the ArcGIS Hillshade tool, 

shadows are estimated for a sample area of San Jose for every hour of the 21st day of every 

month. Areas in shadows are assigned a value of 1, and aggregate values are analyzed to assess 

the reduction in solar access for residential rooftops that face south, southwest, or southeast. 

Solar access is reduced 13% to 16% by building components and trees within their own parcel

and less than 2% by buildings and trees from nearby parcels.

Additional studies use a variety of the previously discussed methods to estimate rooftop space

available for PV. Liddell (2010) presents a method for estimating suitable rooftop space using 

shading, slope, aspect, and size as inputs. This study focuses exclusively on the Seattle, 

Washington, area and does not provide any details about how the three criteria are used to 

determine rooftop space. No results had been presented as of 2010, the date of Liddell’s 

presentation. Kodysh et al. (2013) use LiDAR data to calculate slope, aspect, and solar radiation 

values—determined by cloud cover and transmissivity data—for Knox County, Tennessee. 

Building footprints are extracted from a DEM and buffered by 25 meters to lessen the amount of 

data needing processing while still capturing shading influences from nearby buildings and trees. 

Results from the analysis are on a building-specific level, and no aggregate results are presented.
14

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2 Patent Search

In addition to the studies summarized above, we conducted a detailed patent search and

identified six U.S. patents that are relevant to rooftop-area estimation methods for PV. Table 7

summarizes the patent information found.

Table 7. Results of Patent Search

Patent Data Source Product Notes

University of Hawaii 

(Meder et al. 2007)

Aerial photos, DEMs, 

walking and driving 

surveys

Assessment of solar insolation 

values for various rooftop types

Study area is 

Mapunapuna district, 

Hawaii

Ecometrics, LLC 

(Woro 2009)

DEM from LiDAR 

data or aerial photos

Tabular results of solar 

insolation values on a buildingspecific level

Shadow simulations 

run against building 

data at 6-minute 

intervals

Ecometrics, LLC

(Woro 2010)

DEM from LiDAR 

data or aerial photos

Tabular results of solar 

insolation values on a buildingspecific level

Adds solar irradiance 

component to Woro 

(2009) patent

CH2M Hill

(Hochart et al. 2009, 

Palizzi 2008)

LiDAR data, stereo 

photos, aerial photos, 

construction data, 

survey and 

measurement data

Method for estimating usable 

rooftop space for solar; resulting 

data incorporated in online 

mapping tool

Does not incorporate 

slope or aspect data 

in calculations

Geostellar

(Levine et al. 2011)

LiDAR, stereo 

imagery, and color 

infrared imagery

Quantifies the levelized cost of 

energy (LCOE) based on 

shadow, slope and orientation of 

facets, as well as economic 

suitability based on tariffs, load,

and incentives

Available through 

Web and iOS app, 

integrated with social 

networks

Augenbraun et al. 

(2012) Aerial photos

Method for estimating “power 

flux” to create an executable 

software package and mobile 

application

Unique feature is 

using shadows to 

identify obstructions 

A 2007 patent for the University of Hawaii (Meder et al. 2007) uses aerial photographs and 

DEMs to identify rooftop areas within the study area of the Mapunapuna district in Hawaii. 

Researchers identified six sample neighborhoods and conducted walking and driving surveys to 

take measurements and verify rooftop characteristics identified in the aerial photography. Each 

rooftop is classified into a specific architectural type, such as gable, hip, or shed. For each 

rooftop type in the sample areas, the slope, horizontal global insolation, and rooftop orientation 

are calculated. ArcGIS Solar Analyst is used to estimate solar radiation values for the average 

day of the average year. The final product produced is an assessment of solar insolation values 

for various rooftop types in the sample areas.

A 2009 patent for Ecometrics, LLC (Woro 2009) provides a detailed method for estimating the 

solar potential of rooftops. Building footprints are extracted from a DEM derived from either 

LiDAR data or aerial photography. Shadow simulations are run against the building data at 6-

minute intervals by triangulating the sun angle against the building data; they are then aggregated 
15

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to identify the ideal rooftop areas for PV. The results are combined with size, slope, and 

orientation data to provide tabular results of solar insolation values on a building-specific level. 

A second patent for Ecometrics, LLC (Woro 2010) builds on the first to include a solar 

irradiance component. The 6-minute shadow simulations are reclassified to create a normalized 

shade file for each interval. Each interval file is summed to create an irradiance-weighted shade 

file. The irradiance-weighted shade file is divided by the time frequency interval to determine a 

solar access value for each rooftop area. The solar access values are used in combination with 

slope, orientation, and rooftop material to identify suitable areas for PV construction. 

The CH2M Hill online solar mapping program, S.A.F.E., is included in their 2009 patent 

(Hochart et al. 2009, Palizzi 2008) to develop a method for estimating usable rooftop space for 

solar installation. The resulting data are incorporated in an online mapping tool. Rooftop data are

obtained from a variety of sources, including LiDAR data, stereo photography, aerial 

photography, construction data, and survey and measurement data. A time-series shading 

analysis is performed for multiple sun angles and the resulting data are aggregated to determine 

whether a particular roof area meets an undisclosed threshold for solar suitability. The solar 

energy potential is calculated from the total rooftop area meeting that threshold. Areas 

determined to be suitable for rooftop solar installation are loaded into an interactive mapping 

platform and made available to the public. Users of S.A.F.E can query specific buildings to learn 

about potential energy production, savings potential, carbon dioxide reduction, and return on 

investment. 

Geostellar’s 2011 patent (Levine et al. 2011) performs a suitability analysis for a variety of 

renewable energy technologies on a regional scale, with an emphasis on solar. Specific to the 

solar component of this patent, the researchers use DSMs to determine the amount of time that a 

1-square-meter pixel is in sunlight, both on a monthly timeframe and a daily timeframe. The 

number of minutes a region is in sunlight is divided by the total number of minutes of daylight to 

calculate solar availability. The results for each month are summed to calculate an average solar 

availability for the year. These data, combined with rooftop and ground slope, azimuth, buildable 

area, utility rates, incentives, and load data, are applied to a parcel data layer to determine the

specific costs and benefits of various solar equipment and financing options for each parcel 

of land. 

In a 2012 private-party patent (Augenbraun et al. 2012), researchers identify a method of 

estimating “power flux”—a measure of light energy—to create an executable software package 

and mobile application. In this method, the individual roof planes are identified manually and 

their orientation recorded. These roof planes are used to create a 3-D building model. Aerial 

photographs are used to identify shadows cast within the study area, and the shadows are used to 

identify likely obstructions. This method of using known shadows to locate obstructions is a 

major difference between this analysis and the majority of other studies, which use known 

obstructions to estimate likely shadow patterns. Using the obstructions generated from 

photography, ray traces are run against the 3-D model to create a full shadow map and calculate 

the power flux. “Keep-out regions” are manually defined and are determined by local 

requirements and legal restrictions, solar technology limitations, and potential economic 

payback. The final areas of rooftop solar suitability are loaded into an executable software

program and mobile application and made available to external users.
16

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3 Validation of Results

Using results from the literature is challenging because few studies include a process for 

validating the particular rooftop-area estimation method. Thus, researchers have little 

information about the accuracy of estimates from most studies. Of the 35 studies included in this 

review, only seven mention significant efforts to validate results. The validation methods of 

those that compare results with other sources vary widely in scale and type. Some studies 

validate against other computer models, some against existing solar resource data, and others by 

physically inspecting actual buildings. Table 8 summarizes the validation methods used as well 

as their advantages and disadvantages.

Table 8. Advantages and Disadvantages of Validation Methods Used in Reviewed Literature

Method No. Advantages Disadvantages

Compare to 

existing computer 

files or models 

2

High availability of resources

Low cost

Not time consuming

Inability to know accuracy of indirect 

sources

Compare to 

installed systems 2 High-quality data to compare against

Low availability of resources

Compares to installed infrastructure 

with economic constraints, not total 

potential infrastructure based strictly on 

rooftop characteristics

Physical/on-site 

inspections 4 High-quality data to compare against

Can be expensive

Time consuming

Need equipment and permission to 

take measurements

The analysis of southeastern Ontario (Wiginton et al. 2010) covers an area of 48,000 square 

kilometers and a population of 1.9 million. Much of the study area did not have readily available 

building footprint data, so the analysts use Feature Analyst to extract building footprints from 

aerial imagery. The Feature Analyst results are compared with areas that did have existing 

building footprint shapefiles, and an error of 15% is found in extracting buildings. However, 

when accounting for positive and negative errors, the total difference is 2%. No additional 

validation process is conducted in this study for the actual rooftop suitability measurements.

The MIT analysis of Cambridge, Massachusetts (Jakubiec and Reinhart 2013), reviews several 

online solar-potential maps. These online maps use many of the same methods included in this 

review, including using constant-value methods and GIS tools such as Solar Analyst. Jakubiec 

and Reinhart compare their results with the results of a Solar Analyst method, flat-roofassumption method, and constant-value method. Accounting for climate data, solar resource, and 

building component data as well as verifying against two existing PV systems, they find that 

Solar Analyst slightly underestimates the amount of energy potential, while the flat-roof 

assumption and constant-value methods greatly overestimate the energy potential. They conclude 

that their detailed method of rooftop availability more accurately estimates suitable rooftop space 

than any of the other methods researched.

Most studies that discuss validation compare their rooftop measurement results with those of 

actual buildings. In some cases—such as in the Kingston, Ontario (Nguyen and Pearce 2012),
17

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and Georgetown, Malaysia (Latif et al. 2012), studies—very few buildings are verified. 

Researchers in the Kingston study measure shadows at one building during both a morning hour 

and an afternoon hour to validate their results on the study area of 100 buildings. Georgetown 

researchers identify three buildings in their study area with either existing or planned PV to 

compare their modeled energy generation. The Newark, Delaware, study (Zhang et al. 2009)

includes walking surveys to verify areas with significant shading and rooftop obstructions with 

results from digitizing rooftop area from aerial photography.

The Portland analysis (Bright and Burman 2010) includes site visits to many of the buildings in 

the study area with questionable availability of rooftop area. These site visits include actual 

rooftop measurements as well as analysis of the annual shading effects on the roof using a suneye system. The researchers do not discuss how many rooftops are ground-truthed for accuracy. 

The Chandler, Arizona, study (Jo and Otanicar 2011) includes a visual ground inspection of 150 

of the 932 analyzed buildings to validate rooftop obstructions and area estimates with the 

satellite imagery used in the analysis.

Validation using data from actual buildings is a large component of NREL’s effort to develop an 

improved method (as discussed in Section 5).
18

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4 NREL Method

The literature contains a wide variety of assumptions related to determining ideal rooftop space 

for PV. Based on the literature review, as well as personal communications with solar technology 

analysts and installers, NREL determined a core set of guidelines, the “NREL method,” that are 

intended to replicate industry best practices (Table 9).

In contrast to much of the literature reviewed in this report, the NREL method does not make 

assumptions about ideal rooftop characteristics other than the broad specifications outlined in 

Table 9. This analysis aims to provide information about the variety of rooftop space available 

and allow users of the data to extract the total amount of ideal rooftop space available for PV 

depending on their particular research questions. Providing the data in this manner allows for a 

greater range of research with various PV technologies as well as the ability to adapt research 

with this data set as PV technology continues to become more efficient.

Table 9. Assumptions in Reviewed Literature and NREL Method

Range of Assumptions from Reviewed Literature NREL Assumptions

Slope

Maximums vary from 0 (flat roofs only) to 45–75 

degrees; others define ideal slope as equal to or 

within 15 degrees of local latitude

Maximum of 60 degrees

Orientation Varies from south-facing only to ranges within east–

south–west Range from east–south–west

Shade Extreme variation in definitions and methodologies 

for estimating shade

Rooftop in sunlight for the minimum 

number of hours required for 80% 

generation, estimated regionally

Size Varies from no size requirements to minimum 

contiguous areas of 9 to 100 m2 Minimum of 10 contiguous m2

Figure 1 outlines NREL’s method. It uses 1-meter resolution LiDAR data and building footprint 

data provided by the U.S. Department of Homeland Security (DHS).

2 It uses ArcGIS tools to 

model slope, orientation, and shading characteristics of roof panels. The NREL method produces 

a data set listing roof panels with a slope less than 60 degrees, an orientation ranging anywhere 

from east to west, the number of hours per day in sunlight averaged annually, and size.

 2 DHS has made data available to government agencies for more than 120 U.S. cities.
19

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Figure 1. NREL method for processing LiDAR data

The shading component of the NREL method is calculated by running the Hillshade tool in 

ArcGIS for every daylight hour on March 21, June 21, September 21, and December 21. The 

shade files are reclassified into a binary value to determine which hours of the day the roof panel 

is in sunlight. All hours for individual months are added together to determine a daily sunlight 

availability, and then the four months are added together and divided by four to estimate the 

annual average sunlight availability.

The number of hours a rooftop should be in sunlight to produce a minimum threshold of energy 

is determined on a city-by-city basis using NREL’s System Advisor Model (SAM).3 SAM 

estimates the total number of daylight hours a rooftop in a specific region would need to be in 

sunlight to produce 80% generation, a best-practice threshold derived from conversations with 

solar installers and research analysts. Using this value for identifying the minimum threshold for 

sunlight hours allows the rooftop model to create more region-specific results regarding sunlight 

criteria.

The slope and orientation components are calculated with the ArcGIS Slope and Aspect tools. To 

account for edge error, all grid cells with a slope greater than 60 degrees are eliminated from the 

data set. The roof planes are extracted from the aspect file by selecting all grid cells with the 

same orientation, and the slope is determined for each roof plane by calculating a zonal average 

of the slope grid cells within each plane. Areas of at least 10 square meters that meet NREL’s 

assumptions for slope, orientation, and sunlight requirements are considered suitable for PV.

The final data set presents an aggregated value of the total amount of rooftop space available for 

PV as well as the total number of buildings with at least some portion of rooftop space available. 

 3 For more information, see sam.nrel.gov. 
20

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The data are intended to be used in the aggregate form to aid in subsequent regional analyses.

Additionally, the original generated hillshade, slope, and aspect files are maintained so that 

analysis involving varying thresholds of suitability can be revisited.

NREL conducted a thorough literature review to identify how others have identified rooftop 

space suitable for PV and to develop a set of best practices for this type of analysis. The NREL 

method is very similar, therefore, to much of the previously discussed research. However, a

significant difference between the NREL method and many of the methods described in the 

literature is that the NREL method focuses exclusively on the physical characteristics of the 

rooftop. Weather and climate data are not included, and the energy potential is not calculated. 

This allows other analysts to use a comprehensive data set of rooftops and maintain the ability to

customize their research based on varying PV technologies.

The NREL method also emphasizes a technique that is replicable on a national scale4. Many 

other studies employ techniques that are more detailed than the NREL method in how sunlight 

and shade obstructions are identified. The level of detail in these studies provides a strong 

prediction of sunlight availability on rooftops but makes it difficult to estimate rooftop 

availability estimates throughout the country due to the large amounts of data and intensive 

computer processing requirements.

 4 Currently DHS LiDAR data is available for 120 urban and suburban areas in the U.S. Data from these cities will be 

used to extrapolate rooftop characteristics to other metropolitan areas throughout the nation.
21

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5 Validation of NREL Method

Validation of results obtained using the NREL method is a key component of our analysis. To 

accomplish this, solar installation data were obtained from Trinity Solar in New Jersey, Namaste 

Solar in Colorado, and the California Public Utilities Commission in California. Overall, 205 PV

arrays across the three states were used to validate the results of this analysis. We evaluated 118 

arrays in the Newark and Trenton metropolitan regions, 43 arrays in the Denver/Boulder 

metropolitan region, and 44 arrays in the Anaheim metropolitan region (Figure 2).

Figure 2. Cities included in NREL validation

Trinity Solar provided the most comprehensive data set in the form of as-built drawings detailing 

the location of installed panels, their orientation and slope, and the minimum number of hours 

the panels were estimated to be in sunlight based on initial Solar Pathfinder measurements. 

Because of the exceptional detail of data provided by Trinity, half of the New Jersey data set was 

used to calibrate the model initially, and the full data set was then used to validate the results.

The calibration step of the validation process was intended to ensure that (1) model thresholds 

were appropriately set to capture the rooftops that a solar installer deemed appropriate for PV 

and (2) unsuitable rooftops were excluded. This step was included specifically for use in 

determining the number of hours a roof is in sunlight. The ArcGIS Hillshade tool returns a value 

of 0 to 254, which is directly correlated to the percentage of illumination each grid cell receives. 
22

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Through the calibration process, it was determined that grid cells with a minimum value of 50% 

to 70% illumination were considered to be in sunlight depending on the month, while 

illumination values lower than those thresholds were considered to be in shade.

Of the 118 PV arrays evaluated in the Trinity data set, 26 required that trees be cut to install 

panels. Of the remaining 92 arrays, 65% showed modeled results within 5 degrees of Trinity’s 

slope measurements, and 89% of the modeled results were within 10 degrees. Only 10 arrays in 

the validation data set showed a modeled slope that was more than 10 degrees different from the 

Trinity measurements. The average difference in slope between Trinity’s measurements and the 

modeled results was 4.4 degrees. Only 2 of the 92 arrays modeled an orientation direction 

different than the Trinity measurements.

Additionally, 22 of the 26 arrays that required trees be cut showed the results to be incompatible 

with PV either based on erroneous slope or sunlight estimates, or simply because they did not 

provide the minimum required area. The error in slope estimates for these arrays is due to the 

inability of the first-return LiDAR data to estimate accurately the elevation changes in the roof 

due to obstructions by trees or other shade-casting elements. The negative validation of these 22

arrays verifies that the NREL method will in fact exclude areas with high obstruction. 

Four of the arrays identified for tree cutting by Trinity installers did not model significant 

shading impedance. Two of the roofs showed partial shading in the model, but results showed an 

acceptable amount of unshaded area for installation. One customer did not take Trinity’s 

recommendation for tree trimming—because the tree belongs to a neighbor—and installed a 

panel on only the portion of the roof left unshaded by the tree. A third array that did not show 

shading constraints in the model was one of three arrays installed on that particular roof. The 

other two arrays did show shading constraints, while the third was installed in an area of the roof 

not originally affected by tree shading.

As noted in the literature review, tree-shading changes over time and will add unavoidable error 

in the model. The LiDAR data for New Jersey were collected in 2007 and 2008, while most 

installation assessments performed by Trinity were conducted in 2011 and 2012. Additionally, 

the time of year when the LiDAR data were collected impacts the results of the shading analysis. 

DHS reported that many cities in their data set collected LiDAR data in the spring when trees 

had full foliage, but the exact timing of the data collection for all cities is unknown.

There were 43 arrays used for verification in Colorado. Because documents for these buildings 

provide only an address, the capacity of the system (kW), and incomplete data for the measured 

slope, the exact location of the array and orientation could not be verified. One of these buildings 

was used for negative validation, having had a solar installer deny PV for that residence based on 

the amount of tree shading it received. That house also had too few sun hours in the NREL 

method to be a desirable location for PV. Of the 33 arrays with slope data in the Colorado data

set, 70% had a modeled slope within 5 degrees of the measured slope, and 94% had a modeled 

slope within 10 degrees. The average difference between the measured data and modeled data 

was 3.6 degrees.

The data set from the California Public Utilities Commission contains data on the capacity of 

each system (kW) and its location, as well as the slope and orientation of the PV array. This data
23

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set was more difficult to validate against because the slope data refer to the tilt of the array, not 

necessarily the slope of the roof. Of the 44 arrays used in the validation, 8 were flat-roof 

buildings with tilted arrays. 75% of the buildings had modeled slopes that were more than 5

degrees different from the measured slope, and 86% of the buildings were within 10 degrees. 

The arrays had an average slope difference of 4.9 degrees. All of the arrays matched between the 

measured and modeled orientation.

The New Jersey and California data sets provide the panel manufacturer, model, and number of 

panels installed on the building. These values were used to calculate the actual area required for 

PV. Using these dimensions, 65% of the model results in New Jersey met the minimum size 

requirements for the installed panels, and 98% of the California model results met the minimum 

size requirements. The Colorado data set did not provide panel models or specifications, so 

assumptions about the footprint size of the array had to be made. Each building in the data set 

was validated assuming a standard efficiency of 15% and a panel size of 1.63 square meters. 

Assuming standard efficiency, 79% of modeled results provided at least the minimum amount of 

area required for the actual installed panels.

The results of the validation process show 89% of the modeled results were within 10 degrees of 

the actual slope (Figure 3), 96% of modeled results showed the same orientation as the actual 

orientation, and 99% of modeled results had the required minimum number of sun hours for PV 

to produce 80% generation (Table 10). All arrays used in the validation process showed at least a 

portion of the rooftop was suitable for PV, and 79% showed an area at least the size of the actual 

installed system (Figure 4).
24

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National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 10. Results of NREL Validation

New 

Jersey Colorado California Total

Total systems tested for positive validations

Slope n = 92 n = 33 n = 44 n = 169

Model within 0-5 degrees 60 (65%) 23 (70%) 33 (75%) 116 (69%)

Model within 5-10 degrees 22 (24%) 8 (24%) 5 (11%) 35 (21%)

Model within 10-15 degrees 8 (9%) 1 (3%) 3 (7%) 12 (7%)

Model within 15-20 degrees 0 (0%) 1 (3%) 2 (5%) 3 (2%)

Model within 20-25 degrees 1 (1%) 0 (0%) 0 (0%) 1 (1%)

Model > 25 degrees different 1 (1%) 0 (0%) 1 (2%) 2 (1%)

Orientation n = 92 n = 0 n = 44 n = 136

Model within same orientation 90 (98%) N/A 44 (100%) 134 (99%)

Model outside orientation 2 (2%) N/A 0 (0%) 2 (1%)

Sun hours n = 92 n = 42 n = 44 n = 178

Hours modeled sufficient for PV 92 (100%) 40 (95%) 44 (100%) 176 (99%)

Hours modeled not sufficient for PV 0 (0%) 2 (5%) 0 (0%) 2 (1%)

Size n = 79 n = 42 n = 44 n = 165

Modeled results show suitable area 51 (65%) 33 (79%) 42 (95%) 126 (76%)

Model within 0-10% of actual size 10 (13%) 3 (7%) 1 (2%) 14 (8%)

Model within 10-20% of actual size 6 (8%) 0 (0%) 1 (2%) 7 (4%)

Model within 20-30% of actual size 6 (8%) 0 (0%) 0 (0%) 6 (4%)

Model within 30-40% of actual size 2 (3%) 4 (10%) 0 (0%) 6 (4%)

Model within 40-50% of actual size 0 (0%) 0 (0%) 0 (0%) 0 (0%)

Model within 50-60% of actual size 1 (1%) 0 (0%) 0 (0%) 1 (1%)

Model within 60-70% of actual size 1 (1%) 0 (0%) 0 (0%) 1 (1%)

Model within 70-80% of actual size 2 (3%) 0 (0%) 0 (0%) 2 (1%)

Model within 80-90% of actual size 0 (0%) 1 (2%) 0 (0%) 1 (1%)

Model within 90-100% of actual size 0 (0%) 1 (2%) 0 (0%) 1 (1%)

Total systems tested for negative validations

Tree shading n = 26 n = 1 n = 0 n = 27

Modeled results show shading on roof 22 (85%) 1 (100%) N/A 23 (85%)
25

This report is available at no cost from the 

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at www.nrel.gov/publications.

Figure 3. Distribution of difference in slope

Figure 4. Distribution of difference in size
26

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

6 Conclusions

Identifying the amount of rooftop area that is suitable for PV is crucial for conducting several 

different types of analyses; however, there is no consensus method for estimating that value in 

the literature. In this report, we provide a review of the literature and building on this review, we 

present a new method, the “NREL method” for estimating rooftop suitability for PV. 

The NREL method presented in this report uses LiDAR and building footprint data from the 

DHS to model shade, slope, aspect, and size. Our next step is to apply the validated NREL 

method to data across the more than 120 U.S. cities with available data from the DHS. 

Ultimately, we intend to employ statistical methods to extend our analysis to other areas across 

the United States and create a national database of available rooftop space for PV. This database 

will be useful for estimating rooftop PV market potential to identify promising areas to target 

and other applications. With the database, users will be able to set thresholds for several criteria. 

For example, database users will be able to include only areas on rooftops with less than a 

specified slope or decide whether east- and west-facing roofs should be included with southfacing roofs. This approach taken with the NREL method aims to eliminate some assumptions 

inherent in much of the literature about ideal placement properties and allow more flexibility in 

future analyses. 

A key distinguishing feature of the NREL method for estimating rooftop suitability for PV is the 

thorough calibration and validation process we conducted. This report presents our findings from 

this initial research, which will serve as the foundation for future work on rooftop suitability 

for PV.
27

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

References

Anders, S.; Bialek, T. (undated). Technical Potential for Rooftop Photovoltaics in the San Diego 

Region. Energy Policy Initiatives Center - University of San Diego School of Law and San 

Diego Gas & Electric.

Anderson, K.H.; Coddington, M.H.; Kroposki, B.D. (2010). "Assessing Technical Potential for 

City PV Deployment using NREL's In My Backyard Tool." Prepared for 35th IEEE Photovoltaic 

Specialists Conference, June 20–25, 2010. Honolulu, Hawaii.

Armanino, D.; Johnson, G. (undated). "Phoenix/Scottsdale Solar Rooftop Capacity." County of 

Marin Community Planning Department.

Augenbraun, J.; Cammack, K.; Johnson, D.K.; Sun, J.; Chu, A.C. U.S. Patent No. 2012/0035887 

A1 (9 February 2012).

Bright, J.; Burman, K. (2010). "Portland Ecodistrict Solar Site Assessment." National Renewable 

Energy Laboratory for Solar Cities America.

Chanikarn, Y; Mojtaba, N. (2010). "Quantification of Available Solar Irradiation on Rooftops 

using Orthophotograph and LiDAR Data." Prepared for the Fourth National Conference of 

IBPSA-USA, August 11–13, 2010. New York City, New York.

Chaudhari, M.; Frantzis, L.; Hoff, T.E. (2004). PV Grid Connected Market Potential Under a 

Cost Breakthrough Scenario. 2004-117373. Navigant Consulting.

Compagnon, R. (2004). "Solar and Daylight Availability in the Urban Fabric." Energy and 

Buildings, doi:10.1016/j.enbuild.2004.01.009.

Denholm, P.; Margolis, R. (2008). Supply Curves for Rooftop Solar PV-Generated Electricity for 

the United States. NREL/TP-6A0-44073. Golden, CO: National Renewable Energy Laboratory.

Eiffert, P. (2003). Non-Technical Barriers to the Commercialization of PV Power Systems in the 

Built Environment. NREL/TP-550-31976. Golden, CO: National Renewable Energy Laboratory.

Frantzis, L.; Ghosh, A.; Rogers, M.; Kern, E. (1998). "The Potential for Building-Integrated 

Photovoltaics within the Minneapolis/St. Paul Metro Area: Critical Solar Access/Installation 

Issues." Prepared for the 2nd World Conference and Exhibition on Photovoltaic Solar Energy 

Conversion, July 6–10, 1998. Vienna, Austria.

Frantzis, L.; Graham, S.; Paidipati, J. (2007). California Rooftop Photovoltaic (PV) Resource 

Assessment and Growth Potential by County. CEC-500-2007-048. Navigant Consulting.

Hochart, S.E.; Miller, R.C.; Nugent, N.H.; Stoppenhagen, S.A. U.S. Patent No. 2009/0177458 

A1 (9 July 2009).
28

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Huang, Y.; Yu, B.; Hu, Z.; Wu, J.; Wu, B. (2012). "Locating Suitable Roofs for Utilization of 

Solar Energy in Downtown Area using Airborne LiDAR Data and Object-Based Method: A 

Case Study of the Lujiazui Region, Shanghai." Prepared for the 2012 Second International 

Workshop on Earth Observation and Remote Sensing Applications, June 8–11, 2012. Shanghai, 

China.

IEA - International Energy Agency. (2001). Potential for Building Integrated Photovoltaics. 

IEA-PVPS T7-4. Paris: International Energy Agency.

Jakubiec, J.A.; Reinhart, C.F. (2013). "A Method for Predicting City-Wide Electricity Gains 

from Photovoltaic Panels based on LiDAR and GIS Data Combined with Hourly Daysim 

Simulations." Solar Energy (93); pp. 127–143.

Jo, J.H.; Otanicar, T.P. (2011). "A Hierarchical Methodology for the Mesoscale Assessment of 

Building Integrated Roof Solar Energy Systems." Renewable Energy (36); pp. 2992–3000.

Jofierka, J.; Kanuk, J. (2009). "Assessment of Photovoltaic Potential in Urban Areas using OpenSource Solar Radiation Tools." Renewable Energy, doi:10.1016/j.renene.2009.02.021.

Johnson, G.; Armanino, D. (2004). Solar 2004 Conference: GIS Tools for Community 

Development Applications. San Rafael, CA: County of Marin Community Development Agency.

Kodysh, J.B.; Omitaomu, O.A. (undated). "A GIS-based Methodology for Assessing Rooftop 

Solar Energy Potential." Poster. University of Toledo and Oak Ridge National Laboratory.

Kodysh, J.B.; Omitaomu, O.A.; Bhaduri, B.L.; Neish, B.S. (2013). "Methodology for Estimating 

Solar Potential on Multiple Building Rooftops for Photovoltaic Systems." Sustainable Cities and 

Society, doi:10.1016/j.scs.2013.01.002.

Ladner-Garcia, H.P.; O'Neill-Carrillo, E. (2009). "Determining Realistic Photovoltaic Generation 

Targets in an Isolated Power System." Prepared for 2009 IEEE Power & Energy Society General 

Meeting, July 26–30, 2009. Calgary, Alberta Canada.

Latif, Z.A.; Zaki, N.A.M.; Salleh, S.A. (2012). "GIS-based Estimation of Rooftop Solar 

Photovoltaic Potential using LiDAR." Prepared for the 2012 IEEE 8th International Colloquium 

on Signal Processing and its Applications, March 23–25, 2012. Melaka, Malaysia.

Lehmann, H.; Peter, S. (undated). "Assessment of Roof and Façade Potentials for Solar Use in 

Europe." Aachen, Germany: Institute for Sustainable Solutions and Innovations.

Levine, D.A.; Dawe, R.B.; Deal, R.B., Dobrzanski, J.T. U.S. Patent Application No. WO 

2011/150319 A2 (1 December 2011).

Levinson, R.; Akbari, H.; Pomerantz, M.; Gupta, S. (2008). Estimating the Solar Access of 

Typical Residential Rooftops: A Case Study in San Jose, CA. Berkeley, CA; Lawrence Berkeley 

National Laboratory.
29

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Levinson, R.; Akbari, H.; Pomerantz, M.; Gupta, S. (2009). "Solar Access of Residential 

Rooftops in Four California Cities." Solar Energy (83); pp. 2120–2135.

Liddell, R.M. (2010). "Estimating Rooftop Solar Electricity Potential in Seattle from LiDAR 

Data." Presentation. Penn State University.

Lukac, N.; Zlaus, D.; Seme, S.; Zalik, B.; Stumberger, G. (2013). "Rating of Roofs' Surfaces 

Regarding Their Solar Potential and Suitability for PV Systems, Based on LiDAR Data." Applied 

Energy (102); pp. 803–812.

Meder, S.E.; Pennetier, O.A.; Ansberry, D.M.; Brunner, I.M.I.M. U.S. Patent No. 7,305,983 B1 

(11 December 2007).

Nguyen, H.T.; Pearce, J.M. (2012). "Incorporating Shading Losses in Solar Photovoltaic 

Potential Assessment at the Municipal Scale." Solar Energy (86); pp. 1245–1260.

Nguyen, H.T.; Pearce, J.M. (2013). "Automated Quantification of Solar Photovoltaic Potential in 

Cities." International Review for Spatial Planning and Sustainable Development (1:1); pp. 57–

70.

Ordonez, J.; Jadraque, E.; Alegre, J.; Martinez, G. (2010). "Analysis of the Photovoltaic Solar 

Energy Capacity of Residential Rooftops in Andalusia (Spain)." Renewable and Sustainable 

Energy Reviews (14); pp. 2122–2130.

Paidipati, J.; Frantzis, L.; Sawyer, H.; Kurrasch, A. (2008). Rooftop Photovoltaics Market 

Penetration Scenarios. Work performed by Navigant Consulting, Inc. Burlington, 

Massachusetts. NREL/SR-581-42306. Golden, CO: National Renewable Energy Laboratory.

Palizzi, A. (2008). "Solar Mapping Web Portal using CH2M Hill S.A.F.E. Methodology." 

Prepared for California Solar Forum, February 1, 2008. Riverside, California.

Root, L.; Perez, R. (2006). "Photovoltaic Covered Parking Lots: A Survey of Deployable Space 

in the Hudson River Valley, New York City, and Long Island, New York." Atmospheric 

Sciences Research Center, The University at Albany.

Santos, T.; Gomes, N.; Brito, M.; Breire, S.; Fonseca, A.; Antonio, J. (2011). "Solar Potential 

Analysis in Lisbon using LiDAR data." Prepared for the 31st EARSeL Symposium and 35th 

General Assembly, May 30-June 2, 2011. Prague, Czech Republic.

Tooke, T.R.; Coops, N.C.; Voogt, J.A.; Meitner, M.J. (2011). "Tree Structure Influences on 

Rooftop-received Solar Radiation." Landscape and Urban Planning (102); pp. 73–81.

Vardimon, R. (2011). "Assessment of the Potential for Distributed Photovoltaic Electricity 

Production in Israel." Renewable Energy (36); pp. 591–594.

Wiginton, L.K.; Nguyen, H.T.; Pearce, J.M. (2010). "Quantifying Rooftop Solar Photovoltaic 

Potential for Regional Renewable Energy Policy." Computers, Environment, and Urban Systems

(34); pp. 345–357.
30

This report is available at no cost from the 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Wittmann, H.; Bajons, P. (1997). "Identification of Roof Areas Suited for Solar Energy 

Conversion Systems." Renewable Energy (11:1), 1997; pp. 25–36.

Woro, A.W. U.S. Patent No. 7,500,391 B2 (10 March 2009).

Woro, A.W. U.S. Patent No. 7,832,267 B2 (16 November 2010).

Zhang, X.; Walker, R.; Salisbury, M.; Hromiko, R.; Schreiber, J. (2009). Creating a Solar City: 

Determining the Potential of Solar Rooftop Systems in the City of Newark. University of 

Delaware, Center for Energy and Environmental Policy.