Residual-corrected TSMixer with a RIME-enhanced decomposition strategy for photovoltaic power prediction - Cao, Lipeng; Shao, Xing; Wang, Cuixiang; Gao, Jun (Electric power systems research, 2026)
•Proposes a RIME-enhanced two-stage decomposition for deep feature extraction.•Bayesian optimization and residual correction enhance the model’s performance.•The model’s excellent balance of accuracy and efficiency is verified by experiments. To address the challenges of volatility and uncertainty in photovoltaic (PV) power prediction, this paper proposes a hybrid prediction model based on two-stage decomposition and residual correction (RC). The model first employs Variational Mode Decomposition (VMD), optimized by the RIME algorithm, for primary decomposition, followed by secondary decomposition of the resulting high-frequency residuals using Complete Ensemble EMD with Adaptive Noise (CEEMDAN) to achieve deep feature extraction. Subsequently, a TSMixer model, fine-tuned by the Bayesian Optimization Algorithm (BOA), is used to predict each component. Finally, the prediction results are rectified through an error correction mechanism. Comprehensive experiments based on datasets from multiple sites with diverse climatic conditions–including Alice Springs and Yulara in Australia, and Xinjiang in China–show that the proposed model demonstrates significant performance advantages over several comparative models, validating its effectiveness, robustness, and generalizability in complex PV power prediction tasks.