Wind speed prediction based on singular spectrum analysis and neural network structural learning

Abstract

High precision wind speed prediction is an important technology to improve the operation and maintenance efficiency of wind power. The wind is intermittent and the characteristics of wind speed data can change with time; however, the majority of previous investigations have focused on the hybrid models with fixed structures without considering the adaptive structural learning. In this paper, an adaptive structural deep learning model is proposed to capture the dynamic characteristics of the wind speed. The proposed model involves two parts: the singular spectrum analysis and the modified adaptive structural learning of neural network. The first part is used for wind speed data feature extraction; the second part, designed by combining the adaptive structural learning of neural networks with the long short-term memory network and the newly-designed mix-way structure layer, is used for forecasting. Four simulation tests and several comparison models are used to analyze the prediction accuracy and stability of the established model. The simulation results show that the established model has the advantages of excellent data-dependent learning ability and prediction performance compared to the benchmark models in wind speed prediction.