Short-term prediction for wind power based on temporal convolutional network
Ruijin Zhu, Wenlong Liao, Yusen Wang
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.