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

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  • Caleb Phillips
  • Ryan Elmore
  • Jenny Melius
  • Pieter Gagnon
  • Robert Margolis

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 $ {\rm km}^2 $ km2 of suitable rooftop space which equates to 25% the current US electricity sales.

Suggested Citation

  • Caleb Phillips & Ryan Elmore & Jenny Melius & Pieter Gagnon & Robert Margolis, 2019. "A data mining approach to estimating rooftop photovoltaic potential in the US," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(3), pages 385-394, February.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:3:p:385-394
    DOI: 10.1080/02664763.2018.1492525
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    Cited by:

    1. Julieta, Schallenberg-Rodriguez & José-Julio, Rodrigo-Bello & Pablo, Yanez-Rosales, 2022. "A methodology to estimate the photovoltaic potential on parking spaces and water deposits. The case of the Canary Islands," Renewable Energy, Elsevier, vol. 189(C), pages 1046-1062.
    2. Mahmoud Dhimish, 2020. "Performance Ratio and Degradation Rate Analysis of 10-Year Field Exposed Residential Photovoltaic Installations in the UK and Ireland," Clean Technol., MDPI, vol. 2(2), pages 1-14, May.
    3. Walch, Alina & Rüdisüli, Martin, 2023. "Strategic PV expansion and its impact on regional electricity self-sufficiency: Case study of Switzerland," Applied Energy, Elsevier, vol. 346(C).
    4. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    5. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).

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