Physics-Guided Transformer for Distributed Photovoltaic Power Forecasting Based on Copula Asymmetric Dependence Analysis

Abstract

Distributed photovoltaic (DPV) power forecasting is essential for grid stability but remains challenging due to strong intermittency and meteorological uncertainty. Existing data-driven models often lack physical interpretability and struggle to capture asymmetric dependence structures, leading to unreliable predictions during extreme weather. This paper proposes a copula-guided transformer (CGT) framework that integrates statistical dependence mining with physics-informed deep learning. Specifically, Gaussian copula is used for global feature screening, whereas Clayton and Gumbel copulas quantify asymmetric tail dependencies—revealing the conditional lower-tail inhibitory effect of rainfall and the upper-tail driving effect of irradiance on power output. These copula-derived parameters are embedded as physical priors into a guided encoder, where a temporal convolutional network (TCN) dynamically regulates attention weights to enhance physical consistency. Validated on real-world DPV data from China, the CGT model offers significant performance advantages. The results demonstrate superior robustness across clear-sky, rainy and high-volatility scenarios by effectively mitigating spurious overestimation and response lag.