Short-term wind power prediction and uncertainty analysis based on VDM-TCN and EM-GMM

• Published in: Frontiers in energy research 2024-07, Vol.12
• Main Authors: Peng, Bo, Zuo, Jing, Li, Yaodong, Gong, Xianfu, Huan, Jiajia, Liu, Ruoping
• Format: Article
• Language: English
• Subjects: Gaussian mixture model (GMM); time sequence convolutional neural network (TCN); uncertainty analysis; variational mode decomposition (VDM); wind power prediction
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2024.1404165

Due to the fluctuating and intermittent nature of wind energy, its prediction is uncertain. Hence, this paper suggests a method for predicting wind power in the short term and analyzing uncertainty using the VDM-TCN approach. This method first uses Variational Mode Decomposition (VDM) to process the data, and then utilizes the temporal characteristics of Temporal Convolutional Neural Network (TCN) to learn and predict the dataset after VDM processing. Through comparative experiments, we found that VDM-TCN performs the best in short-term wind power prediction. In wind power prediction for 4-h and 24-h horizons, the RMSE errors were 1.499% and 4.4518% respectively, demonstrating the superiority of VDM-TCN. Meanwhile, the Gaussian Mixture Model (GMM) can effectively quantify the uncertainty of wind power generation at different time scales.