Adaptive expert fusion model for online wind power prediction

Abstract

Wind power prediction is a challenging task due to the high variability and uncertainty of wind generation and weather conditions. Accurate and timely wind power prediction is essential for optimal power system operation and planning. In this paper, we propose a novel Adaptive Expert Fusion Model (EFM+) for online wind power prediction. EFM+ is an innovative ensemble model that integrates the strengths of XGBoost and self-attention LSTM models using dynamic weights. EFM+ can adapt to real-time changes in wind conditions and data distribution by updating the weights based on the performance and error of the models on recent similar samples. EFM+ enables Bayesian inference and real-time uncertainty updates with new data. We conduct extensive experiments on a real-world wind farm dataset to evaluate EFM+. The results show that EFM+ outperforms existing models in prediction accuracy and error, and demonstrates high robustness and stability across various scenarios. We also conduct sensitivity and ablation analyses to assess the effects of different components and parameters on EFM+. EFM+ is a promising technique for online wind power prediction that can handle nonstationarity and uncertainty in wind power generation.