Ensemble probabilistic wind power forecasting with multi-scale features

Abstract

The uncertainty of wind power forecasting has a significant impact on the decision-making process of power system operators. Herein, to comprehensively characterize this uncertainty, a novel ensemble probabilistic forecasting model is developed in this study. First, the best model inputs were selected to forecast future wind power based on the value of maximum information coefficient, which measures both linear and nonlinear relationships between two random variables. Second, to extract sufficient and useful features from historical wind power data, a multi-scale feature extraction module based on average pooling, convolutional neural network, bidirectional long short-term memory, and attention mechanism was proposed. Third, five multi-scale deep density neural networks with different distribution-based loss functions and the proposed multi-scale feature extraction module were designed as base probabilistic forecasters to characterize the uncertainty from various aspects. Finally, an ensemble framework that combines five multi-scale deep density neural networks was built to determine the optimal ensemble weights for all base forecasters by simultaneously minimizing the pinball loss and continuous ranked probability score. The proposed ensemble model was implemented on four real-world wind power datasets. The results demonstrated the effectiveness of the proposed ensemble strategy and the designed multi-scale feature extraction module for probabilistic wind power forecasting.