Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Load and Power Forecasting · Power Systems and Renewable Energy · Smart Grid and Power Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electric Power, South China University of Technology, Guangzhou 510640, China
Zhuhai Power Supply Bureau of Guangdong Power Grid Co., Zhuhai 519000, China
School of Automation, Central South University, Changsha 410083, China
The Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY 14260-1800, USA
Funding
the Science and Technology Project Foundation of Southern Power Grid Co., Ltd. of China · the Joint Research Fund of Chinese Ministry of Education · the Natural Science Foundation of Hunan Province of China · the National Natural Science Foundation of China
References (49)
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