Research and application of a novel weight-based evolutionary ensemble model using principal component analysis for wind power prediction

Accurate prediction of wind power is crucial for optimizing wind energy utilization and ensuring the secure, costeffective, and stable operation of power systems. To address this, a novel approach is proposed that combines a multi-network deep ensemble model with an improved manta ray foraging optimization algorithm (IMRFO) and feature selection for wind power prediction. Firstly, principal component analysis (PCA) is employed to select the principal component with a cumulative contribution rate of 95 %. Secondly, the ensemble prediction model is constructed in the layer-1, incorporating extreme gradient boosting (XGBoost), gated recurrent unit (GRU), and temporal convolutional network (TCN). Additionally, an error-based weighted fusion method is implemented to combine the outputs of three learners. In the layer-2, random forest (RF) employed to make predictions based on the fused results. To further enhance the predictive capabilities of the model, an enhanced MRFO algorithm, incorporating chaos initialization and Gauss-Cauchy mutation strategy, is added. The experiment shows that the deep ensemble model using PCA and IMRFO outperforms XGBoost, GRU, and TCN in predicting. On four datasets, the RMSE of the proposed model is improved by over 45 % compared to them. These findings strongly support the effectiveness and accuracy of the proposed model in this domain.