Very Short-term Wind Direction Prediction Via Self-tuning Wavelet Long-short Term Memory Neural Network

被引：0

作者：

Tang Z. ^{[1
]}

Zhao G. ^{[1
]}

Cao S. ^{[1
]}

Zhao B. ^{[1
]}

机构：

[1] School of Automation Engineering, Northeast Electric Power University, Jilin, 132012, Jilin

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 15期

基金：

中国国家自然科学基金;

关键词：

Error self- tuning; Long short term memory; Mutual information; Wavelet decomposition; Wind direction forecasting;

D O I：

10.13334/j.0258-8013.pcsee.180925

中图分类号：

学科分类号：

摘要：

The wind direction forecasting is the basis of raising the wind energy conversion rate and the yaw control system security. To construct an accurate wind direction prediction model, a self-tuning wavelet long-short term memory neural network (SWLSTM) algorithm was presented. First, the feature length was selected via the mutual information method. Then, the time and frequency domain information of time series were extracted by the wavelet decomposition. Consequently, the wind direction forecasting model was established with the long-short term memory neural network (LSTM). Additionally, a self-tuning strategy was proposed to improve prediction accuracy. To testify the robustness and the accuracy of the SWLSTM algorithm, the actual wind farm data based experiments were carried out. The experiment results indicate that the SWLSTM algorithm is superior to the other common-used algorithms. The prediction errors are less than 1.73%, which can meet the requirements of wind farm. © 2019 Chin. Soc. for Elec. Eng.

引用

页码：4459 / 4467

页数：8

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