A short-term hybrid wind power prediction model based on singular spectrum analysis and temporal convolutional networks

Abstract

Accurate and stable wind power prediction is the basis of wind energy planning, dispatching, and control in the generation and conversion of the wind power generation industry. A new nonlinear hybrid model, singular spectrum analysis (SSA)-temporal neural network (TCN) is proposed to improve the accuracy of wind power sequence prediction. This method introduces SSA, which decomposes the original wind power sequence into four parts: trend, primary detail components, secondary detail components, and noise. The experiment is conducted under the three conditions of no noise removal, only noise removal, and decomposing original sequences without noise into several components to verify that SSA does help improve the model performance. Moreover, this paper adopts a new TCN network instead of the current most advanced Long Short-Term Memory (LSTM) network. It has a longer actual memory range and is better able to cope with the gradient disappearance. In order to study the advancement of the TCN, the experimental part also includes the performance comparison between the TCN and the LSTM network, as well as the ablation experiment of the TCN. Finally, multi-step prediction is conducted to further demonstrate the fitting ability of the TCN, SSA-TCN, and LSTM network. The results show that (a) the SSA-TCN has the best predictive performance among the involved models; (b) SSA can significantly improve the prediction accuracy; (c) the performance of the TCN is better than that of the LSTM network because of the extended causal convolution structure of the TCN and the residual blocks.