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

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.太阳能光伏（Solar Photovoltaic, PV）部署在可再生能源转型进程中占据核心地位。然而，当前仍缺乏能够有效阐释太阳能光伏部署差异的综合模型。本研究旨在填补这一研究空白，提出两款创新模型：其一为可借助卫星影像估算各社区太阳能光伏部署空间分布的计算机视觉模型，其二为基于43项影响因子预测上述分布的机器学习（machine learning, ML）模型。本研究采用的基于快速区域卷积神经网络（Faster Regions with Convolutional Neural Network, Faster RCNN）的计算机视觉模型，在识别太阳能光伏板时的平均精度均值（mean Average Precision, mAP）达81%，识别屋顶时的平均精度均值达95%。借助该模型，我们对美国科罗拉多州的652795张卫星影像展开分析，结果显示：截至2021年初，科罗拉多州约7%的家庭安装了屋顶光伏系统，该州约2.5%的屋顶面积覆盖有太阳能光伏板。在本次研究构建的16个预测模型中，极端梯度提升（XGBoost）模型表现最优，可解释约70%的屋顶太阳能部署差异。研究同时发现，民主党得票占比、冰雹与强风灾害风险、住宅中位价值、租户占比以及太阳能光伏审批时长，均为影响科罗拉多州屋顶太阳能部署的关键预测因子。本研究可为商业与政策决策提供参考，助力更高效、更公平的电网基础设施投资与分布式能源资源管理。