Short-term Wind Power Prediction Model Based on Encoder-Decoder LSTM

Abstract

We propose a long short-term memory (LSTM) network based encoder-decoder (E-D) model for wind power prediction (WPP). The LSTM-based E-D model is constructed as an auto-encoder for mapping the wind power (WP) time-series into a fixed-length representation, state of the trained E-D LSTM. Then, the representation concatenated with weather forecasting information is used as a new input to another multiple LSTM network to make WPP. Real data collected from a wind farm with capacity of 50 MW of Shan Xi province were used to verify the conclusions. Results illustrate that the proposed method improves the model generalization ability and lowers misspecification risk by utilizing the WP time relationship through auto-encoding (AE) process. Combining extracted representation with weather forecasting information further improves the prediction accuracy.