Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose...

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Bibliographic Details
Published in:Applied sciences 2019-03, Vol.9 (6), p.1108
Main Authors: Liu, Yao, Guan, Lin, Hou, Chen, Han, Hua, Liu, Zhangjie, Sun, Yao, Zheng, Minghui
Format: Article
Language:English
Subjects:
Accuracy
Alternative energy sources
Back propagation
Computer applications
Decomposition
Discrete Wavelet Transform
Electric power grids
Energy resources
Energy sources
LSTM
Maintenance costs
Methods
Neural networks
Noise
Photovoltaics
Propagation
Renewable energy sources
Renewable resources
Time series
Wavelet transforms
Wind farms
Wind power
wind power forecasting
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Summary:A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9061108