Spatiotemporal analysis and forecasting of PV systems, battery storage, and EV charging diffusion in California: A graph network approach

Understanding the dynamics of clean energy adoption is crucial for shaping effective energy policies and strategies. To understand the spatiotemporal variability and bottom-up spontaneity of the diffusion of clean energy technologies, this study examined the spatiotemporal diffusion of Photovoltaic (PV) systems, Battery Energy Storage Systems (BESS), and Electric Vehicle Charging Stations (EVCS) across 1773 postcode areas in California over the past 35 years. The Bayesian change point detection algorithm (BCDA) was initially utilized to identify temporal change points in the evolution of these technologies, revealing their diffusion trends over time. Then, the Louvain community detection algorithm (LCDA) was employed to elucidate spatial diffusion patterns across regions, offering insights into the geographic proliferation of clean energy solutions. Furthermore, to forecast future distributions, the spatiotemporal graph convolutional network (STGCN) model was applied, adeptly capturing the multi-stage, nonlinear characteristics of clean energy diffusion marked by significant spatiotemporal interactions. With historical data and spatial proximity, the STGCN model demonstrated fine forecasting precision (R^2 ≥ 0.78). This study validates the effectiveness of a graph network-based approach for analyzing the diffusion of clean energy technologies. It recommends policies tailored to account for technological maturity, geographic disparities, and diffusion stages, aiming for equitable and sustainable clean energy development in the region.