DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

By Jiafan Yu, Zhecheng Wang, Arun Majumdar, Ram Rajagopal. Joule, 2018

339 citations recorded by the source corpus.

Summary We developed DeepSolar, a deep learning framework analyzing satellite imagery to identify the GPS locations and sizes of solar photovoltaic panels. Leveraging its high accuracy and scalability, we constructed a comprehensive high-fidelity solar deployment database for the contiguous US. We demonstrated its value by discovering that residential solar deployment density peaks at a population density of 1,000 capita/mile2, increases with annual household income asymptoting at ∼$150k, and has an inverse correlation with the Gini index representing income inequality. We uncovered a solar radiation threshold (4.5 kWh/m2/day) above which the solar deployment is “triggered.” Furthermore, we built an accurate machine learning-based predictive model to estimate the solar deployment density at the census tract level. We offer the DeepSolar database as a publicly available resource for researchers, utilities, solar developers, and policymakers to further uncover solar deployment patterns, build comprehensive economic and behavioral models, and ultimately support the adoption and management of solar electricity.

Subfields: Global and Planetary Change, Pollution, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/448d22e2c9e4dc87c40eb328bcc8c5ee29eeb748

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