Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting
<|reference_start|>Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting: 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.<|reference_end|>
@article{gao2024cross-variable,
    title={Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic
  power forecasting},
    author={Jiaxin Gao, Qinglong Cao, Yuntian Chen, Dongxiao Zhang},
    journal={arXiv preprint arXiv:2406.03808},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03808},
    primaryClass={cs.LG cs.AI stat.AP}
}