An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms. (1st October 2022)

• Record Type: Journal Article
• Authors: Yu, Enbo; Xu, Guoji; Han, Yan; Li, Yongle
• Is Part Of: Energy. Volume 256(2022)
• Publication Date: 2022-10-01
• Subjects: Wind speed prediction -- Deep learning -- Convolutional neural network -- Long short-term memory -- Attention mechanism; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124569
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: With the growing focus on renewable energy, wind power is increasingly valued and advocated. In order to guarantee the stability of wind power system dispatch and management, reliable prediction of future wind speeds is essential. In this study, a short-term wind speed prediction model based on cross-channel data convolution, intelligent signal extension and attention mechanisms is proposed to enhance the prediction efficiency. The model first classifies the wind speed signal into IMFs (intrinsic mode functions) and residual data with the EMD (empirical mode decomposition) method, and then divides IMFs into rough prediction part and accurate prediction part according to the signal characteristics. CNN (convolutional neural network) modules are adopted for the rough prediction part to ensure a speedy process, whereas a CNN-AM (attentional mechanism)-LSTM (long short-term memory)-ECA (efficient channel attention) hybrid network is developed to for the accurate prediction part. Through the time-history prediction on measured 10 min average wind speed data, the results show that: (a) The channel-crossing one-dimensional (1D) convolution, intelligent signal extension, and attention mechanisms applied in the proposed model can effectively improve the accuracy of predictions; (b) The proposed prediction model is superior to the compared baseline models in precision and efficiency; and (c) The proposed model features strong migration learning ability for fast application on new datasets. Highlights: A computationally efficient wind speed prediction model is developed. Intelligent padding from model predictions is considered to cope with end effect in signal analysis. Spatial and channel attention mechanisms are applied to improve prediction accuracy. The training, validation, and test sets are established by random sampling to avoid seasonal interference.