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Title: A data mining approach to estimating rooftop photovoltaic potential in the US

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:
 [1] ;  [2] ;  [1] ;  [1] ;  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Univ. of Denver, Denver, CO (United States)
Publication Date:
Report Number(s):
NREL/JA-2C00-66550
Journal ID: ISSN 0266-4763
Grant/Contract Number:
AC36-08GO28308
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
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Policy and Systems Analysis (EPSA)
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; applied statistics; regression; GIS; solar; energy; predictive model
OSTI Identifier:
1462462

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., 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. doi:10.1080/02664763.2018.1492525.
Phillips, Caleb, Elmore, Ryan, Melius, Jenny, Gagnon, Pieter, and Margolis, Robert. 2018. "A data mining approach to estimating rooftop photovoltaic potential in the US". United States. doi:10.1080/02664763.2018.1492525.
@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 = {2018},
month = {7}
}