Short-term prediction of the power of a new wind turbine based on IAO-LSTM

被引：15

作者：

Li, Zheng ^{[1
]}

Luo, Xiaorui ^{[1
]}

Liu, Mengjie ^{[1
]}

Cao, Xin ^{[2
]}

Du, Shenhui ^{[1
]}

Sun, Hexu ^{[1
]}

机构：

[1] Hebei Univ Sci & Technol, Sch Elect Engn, 26 Yuxiang Stree, Shijiazhuang 050018, Peoples R China

[2] Hebei Construct & Investment Grp New Energy Co Ltd, 9 Yu Hua West Rd, Shijiazhuang 050051, Peoples R China

来源：

ENERGY REPORTS | 2022年 / 8卷

基金：

中国国家自然科学基金;

关键词：

Wind power prediction; New wind turbine; Aquila Optimizer; LSTM network; MEMORY; NETWORKS; MODEL;

D O I：

10.1016/j.egyr.2022.07.030

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Short-term wind power forecasting is of great significance to the real-time dispatching of power systems, but the short-term forecasting accuracy of wind power is not high. To this end, this paper proposes a hybrid prediction model that combines the Isolated Forest algorithm, the Synchronous Squeeze Wavelet Transform (SWT) method, the Aquila Optimizer (AO) and the Long Short-term Memory network (LSTM). Firstly, the Isolated Forest algorithm is used to detect abnormal data. Secondly, the SWT method is used to denoise the original power signal of the new wind turbine. Then, the wind power prediction model is established through the long short-term memory network algorithm. The OA is used to optimize the LSTM structure parameters to solve the influence of random parameters on the prediction accuracy. Finally, perform example verification. The results show that the proposed model is effective in power prediction of new wind turbine. (C) 2022 The Author(s). Published by Elsevier Ltd.

引用

页码：9025 / 9037

页数：13

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