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

2018 International Conference of Green Buildings and Environmental Management

土木建筑工程;生态环境科学

Lu, Kuan^1 ; Sun, Wen Xue^2 ; Wang, Xin^1 ; Meng, Xiang Rong^1 ; Zhai, Yong^3 ; Li, Hong Hai^3 ; Zhang, Rong Gui^3

State Grid Shandong Electric Power Research Institute, Shandong, Jinan, China^1

State Grid Zhangqiu Power Supply Company, Jinan, Shandong, China^2

Shandong Luneng Software Technology Co., LTD, Jinan, Shandong, China^3

关键词: Auto encoders;    Encoder-decoder;    Misspecification;    Model generalization;    Network-based;    Prediction accuracy;    Short-Term wind power predictions;    Wind power predictions;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/186/5/012020/pdf
DOI  :  10.1088/1755-1315/186/5/012020

来源: IOP

【 摘 要 】

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.