Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

Abstract

Abstract Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.

Keywords

• Solar PV
• Data mining
• Computer vision
• Region-based convolutional neural network
• Energy transition
• Renewable energy