Short-term prediction for wind power based on temporal convolutional network. (December 2020)

• Record Type: Journal Article
• Authors: Zhu, Ruijin; Liao, Wenlong; Wang, Yusen
• Is Part Of: Energy reports. Volume 6(2020)Supplement 9
• Page Start: 424
• Page End: 429
• Publication Date: 2020-12
• Subjects: Fluctuation -- Short-term prediction -- Wind power -- Temporal convolutional network; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.11.219
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: The fluctuation and intermittence of wind power bring great challenges to the operation and control of the distribution network. Accurate short-term prediction for wind power is helpful to avoid the risk caused by the uncertainties of wind powers. To improve the accuracy of short-term prediction for wind power, the temporal convolutional network (TCN) is proposed in this paper. The proposed method solves the problem of long-term dependencies and performance degradation of deep convolutional model in sequence prediction by dilated causal convolutions and residual connections. The simulation results show that the training process of TCN is very stable and it has strong generalization ability. Furthermore, TCN shows higher forecasting accuracy than existing predictors such as the support vector machine, multi-layer perceptron, long short-term memory network, and gated recurrent unit network.