Day-Ahead Photovoltaic Power Forecasting Based on SN-Transformer-BiMixer

Accurate forecasting of photovoltaic (PV) power is crucial for ensuring the safe and stable operation of power systems. However, the practical implementation of forecasting systems often faces challenges due to missing real-time historical power data, typically caused by sensor malfunctions or communication failures, which substantially hamper the performance of existing data-driven time-series forecasting techniques. To address these limitations, this study proposes a novel day-ahead PV forecasting approach based on similar-day analysis, i.e., SN-Transformer-BiMixer. Specifically, a Siamese network (SN) is employed to identify patterns analogous to the target day within a historical power dataset accumulated over an extended period, considering its superior ability to extract discriminative features and quantify similarities. By identifying similar historical days from multiple time scales using SN, a baseline generation pattern for the target day is established to allow forecasting without relying on real-time measurement data. Subsequently, a transformer model is used to refine these similar temporal curves, yielding improved multi-scale forecasting outputs. Finally, a bidirectional mixer (BiMixer) module is designed to synthesize similar curves across multiple scales, thereby providing more accurate forecast results. Experimental results demonstrate the superiority of the proposed model over existing approaches. Compared to Informer, SN-Transformer-BiMixer achieves an 11.32% reduction in root mean square error (RMSE). Moreover, the model exhibits strong robustness to missing data, outperforming the vanilla Transformer by 8.99% in RMSE.