HAI Policy Briefs
Using AI to Understand Residential Solar Power
Residential solar panel usage is growing rapidly, as more households use photovoltaic (PV) technology to convert sunlight into electricity. But the deployment of residential solar PV has been highly unequal across the United States. Studying the spread of solar PV technology is vital to identifying and tackling barriers to adoption. In this brief, we present computer vision as an essential technique that can help policymakers understand residential solar usage. Our research uses computer vision to build a nationwide dataset to capture information about solar PV deployment in the United States across time and geography in an automated and scalable manner.
➜ The United States has experienced a rapid adoption of residential solar photovoltaics (PV) technology, an electricity generation method that is a crucial part of efforts to decarbonize the energy sector.
➜ We created a publicly available dataset of historical satellite and aerial imagery and a computer vision model to study variations in solar PV adoption over time in U.S. households. We found that lower-income communities are more delayed in adoption.
➜ The effectiveness of incentive mechanisms for spurring adoption varies widely for different socioeconomic groups; while property tax incentives led to relatively more solar PV adoption in higher-income communities, performance-based incentives were more successful in boosting solar adoption among lower-income communities.
➜ Policymakers should leverage open datasets and AI models to gain crucial insights into solar PV adoption and customize policies and financial incentives accordingly. Adopting computer vision techniques can not only help accelerate solar PV adoption but also make the clean energy transition more equitable.