  Photovoltaic (PV) power forecasting plays a crucial role in optimizing the
operation and planning of PV systems, thereby enabling efficient energy
management and grid integration. However, un certainties 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 an ENhanced Transformer module to
capture complex interactions of various features in PV systems, and utilizes a
linear module to learn trend information in PV power. Diverging from
conventional time series-based Transformer models that use cross-time Attention
to learn dependencies between different time steps, the Enhanced Transformer
module integrates cross-variable Attention to capture dependencies between PV
power and weather factors. Furthermore, PV-Client streamlines the embedding and
position encoding layers by replacing the Decoder module with a projection
layer. Experimental results on three real-world PV power datasets affirm
PV-Client's state-of-the-art (SOTA) performance in PV power forecasting.
Specifically, PV-Client surpasses the second-best model GRU by 5.3% in MSE
metrics and 0.9% in accuracy metrics at the Jingang Station. Similarly,
PV-Client outperforms the second-best model SVR by 10.1% in MSE metrics and
0.2% in accuracy metrics at the Xinqingnian Station, and PV-Client exhibits
superior performance compared to the second-best model SVR with enhancements of
3.4% in MSE metrics and 0.9% in accuracy metrics at the Hongxing Station.
