Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, enabling efficient energy management and grid integration. However, uncertainties caused by fluctuating weather conditions and complex interactions between different variables pose significant challenges to accurate PV power forecasting. In this study, we propose PV-Client (Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting) to address these challenges and enhance PV power forecasting accuracy. PV-Client employs a linear module to learn trend information in PV power, and employs an ENhanced Transformer module to capture complex interactions in PV systems. Experiments with the real-world PV power dataset have confirmed the SOTA performance of PV-Client in PV power forecasting.
Keywords PV power forecasting, PV-Client, linear, Transformer