Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy

Abstract

Accurate wind power forecasting helps to maximize the utilization of wind energy resources, enhance wind power generation efficiency, and optimize grid operation. This study proposes an innovative mixed-frequency modeling and interpretable base model selection-based ensemble wind power forecasting system. Specifically, the data preprocessing module preprocesses wind speed and wind power data at different frequencies. The mixed-frequency modeling module then constructs 12 mixed-frequency and machine learning models to predict wind power, with a comprehensive evaluation metric to determine their optimal lags. Subsequently, the base model selection module effectively combines the elastic net and Shapley additive explanation methods to identify individual models that contribute significantly to the prediction target as base models. Finally, the ensemble module integrates the optimization algorithms with a machine learning model to ensemble the selected base models. The key findings are as follows: (1) mixed-frequency wind speed and wind power data effectively improve forecasting performance, and (2) the proposed base model selection strategy greatly enhances the accuracy and interpretability of the modeling process. This model could robustly predict two datasets from Inner Mongolian wind farms, with average absolute percentage errors of 2.4505% and 4.8270%, respectively, establishing this as a useful technique for wind power prediction.