Multi-time scale wind speed prediction based on WT-bi-LSTM

Automation School, Central South University, Changsha, 410083, Hunan Province, China

^* Corresponding author: zhifeng.qiu@csu.edu.cn

Abstract

The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.