DeepSolar++: Understanding Residential Solar Adoption Trajectories with Computer Vision and Technology Diffusion Models

Title data

Wang, Zhecheng ; Arlt, Marie-Louise ; Zanocco, Chad ; Majumdar, Arun ; Rajagopal, Ram:
DeepSolar++: Understanding Residential Solar Adoption Trajectories with Computer Vision and Technology Diffusion Models.
In: Joule. Vol. 6 (2022) Issue 11 . - pp. 2611-2625.
ISSN 2542-4351
DOI: https://doi.org/10.1016/j.joule.2022.09.011

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Abstract in another language

Solar photovoltaic (PV) systems are being deployed at a rapid yet non-uniform pace. To explain this heterogeneity across space and time, we applied computer vision to historical satellite and aerial images and constructed a spatiotemporal dataset of PV deployment in the United States. We analyzed the data using a technology diffusion model and found that low-income communities are not only delayed in their PV adoption onset but also saturate more quickly at lower levels. We also found that certain types of incentives are associated with a high saturated adoption level in low-income communities, whereas other types are not. The computer vision model we developed can be scaled to any location on Earth where historical aerial or satellite images are available at a sufficient resolution. Additionally, we make our dataset publicly available as a resource for researchers, policymakers, and other stakeholders to understand PV adoption dynamics and customize policy design.