A data mining approach to estimating rooftop photovoltaic potential in the US
Abstract
This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926 TWh/year of PV energy on 4920 km2 of suitable rooftop space which equates to 25% the current US electricity sales.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Univ. of Denver, Denver, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Policy and Systems Analysis (EPSA)
- OSTI Identifier:
- 1462462
- Report Number(s):
- NREL/JA-2C00-66550
Journal ID: ISSN 0266-4763
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Applied Statistics
- Additional Journal Information:
- Journal Volume: 46; Journal Issue: 3; Journal ID: ISSN 0266-4763
- Publisher:
- Taylor & Francis
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 29 ENERGY PLANNING, POLICY, AND ECONOMY; applied statistics; regression; GIS; solar; energy; predictive model
Citation Formats
Phillips, Caleb, Elmore, Ryan, Melius, Jenny, Gagnon, Pieter, and Margolis, Robert. A data mining approach to estimating rooftop photovoltaic potential in the US. United States: N. p., 2018.
Web. doi:10.1080/02664763.2018.1492525.
Phillips, Caleb, Elmore, Ryan, Melius, Jenny, Gagnon, Pieter, & Margolis, Robert. A data mining approach to estimating rooftop photovoltaic potential in the US. United States. https://doi.org/10.1080/02664763.2018.1492525
Phillips, Caleb, Elmore, Ryan, Melius, Jenny, Gagnon, Pieter, and Margolis, Robert. Wed .
"A data mining approach to estimating rooftop photovoltaic potential in the US". United States. https://doi.org/10.1080/02664763.2018.1492525. https://www.osti.gov/servlets/purl/1462462.
@article{osti_1462462,
title = {A data mining approach to estimating rooftop photovoltaic potential in the US},
author = {Phillips, Caleb and Elmore, Ryan and Melius, Jenny and Gagnon, Pieter and Margolis, Robert},
abstractNote = {This paper aims to quantify the amount of suitable rooftop area for photovoltaic (PV) energy generation in the continental United States (US). The approach is data-driven, combining Geographic Information Systems analysis of an extensive dataset of Light Detection and Ranging (LiDAR) measurements collected by the Department of Homeland Security with a statistical model trained on these same data. The model developed herein can predict the quantity of suitable roof area where LiDAR data is not available. This analysis focuses on small buildings (1000 to 5000 square feet) which account for more than half of the total available rooftop space in these data (58%) and demonstrate a greater variability in suitability compared to larger buildings which are nearly all suitable for PV installations. This paper presents new results characterizing the size, shape and suitability of US rooftops with respect to PV installations. Overall 28% of small building roofs appear suitable in the continental United States for rooftop solar. Nationally, small building rooftops could accommodate an expected 731 GW of PV capacity and generate 926 TWh/year of PV energy on 4920 km2 of suitable rooftop space which equates to 25% the current US electricity sales.},
doi = {10.1080/02664763.2018.1492525},
journal = {Journal of Applied Statistics},
number = 3,
volume = 46,
place = {United States},
year = {Wed Jul 04 00:00:00 EDT 2018},
month = {Wed Jul 04 00:00:00 EDT 2018}
}
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Figures / Tables:
Figure 1: A database of suitable planes underlies the model development. LiDAR data is used to extract rooftop plane information. Planes are classified as suitable if their tilt, azimuth (orientation) and shading is appropriate for PV applications. These data are used directly for development of tilt, azimuth and plane areamore »
Works referenced in this record:
Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling
journal, February 2018
- Gagnon, Pieter; Margolis, Robert; Melius, Jennifer
- Environmental Research Letters, Vol. 13, Issue 2
Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities
journal, July 2017
- Margolis, Robert; Gagnon, Pieter; Melius, Jennifer
- Environmental Research Letters, Vol. 12, Issue 7
Works referencing / citing this record:
Solar PV as a mitigation strategy for the US education sector
journal, March 2019
- Hanus, Nichole L.; Wong-Parodi, Gabrielle; Vaishnav, Parth T.
- Environmental Research Letters, Vol. 14, Issue 4
Figures / Tables found in this record: