A Data Mining Approach to Estimating Rooftop Photovoltaic Potential in the US
Publication Date
7-4-2018
Document Type
Article
Organizational Units
Daniels College of Business, Business Information and Analytics
Keywords
Applied statistics, Regression, GIS, Solar, Energy, Predictive model
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" role="presentation" style="box-sizing: border-box; display: inline; line-height: normal; font-size: 24.64px; text-align: left; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">km2km2 of suitable rooftop space which equates to 25% the current US electricity sales.
Publication Statement
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Recommended Citation
Phillips, Caleb, et al. “A Data Mining Approach to Estimating Rooftop Photovoltaic Potential in the US.” Journal of Applied Statistics, vol. 46, no. 3, 2019, pp. 385–394. doi: 10.1080/02664763.2018.1492525.