An Ensemble Learning Approach for Wind Power Prediction Using Hybrid Deep Learning

Abstract

Wind power prediction is important for utility grid energy scheduling. However, prediction accuracy suffers from poor data quality and limited feature engineering. Therefore, this paper proposes a data-driven ensemble learning framework to deal with these issues. First, the paper divides raw data into four seasonal groups. A preprocessing pipeline integrating the 3σ rule, Hermite interpolation, and a wind power curve modeling method detects and corrects outliers. Second, we develop a two-stage prediction strategy. In the first stage, a hybrid deep learning model including convolutional neural networks, gated recurrent units, and an attention mechanism jointly learns spatio-temporal features, with hyperparameters optimized using the exponential-trigonometric optimization (ETO) algorithm and the input length determined using the partial autocorrelation function. In the second stage, a temporal convolutional network with ETO-optimized hyperparameters predicts the error sequence, capturing multi-scale nonlinear relationships between input features and error dynamics. Experimental results on real-world wind farm data demonstrate that the proposed method achieves a mean RMSE of 5.89±0.11 MW, MAE of 4.05±0.09 MW, MAPE of 7.24±0.18%, and R2 of 0.928±0.008 over 50 independent runs. A representative single run achieves an RMSE of 5.89 MW, MAE of 4.02 MW, MAPE of 6.9%, and R2 of 0.93.