Short-term prediction of wind power based on phase space reconstruction and BiLSTM

被引：17

作者：

Ying Huamei ^{[1
]}

Deng Changhong ^{[1
]}

Xu Zhenghua ^{[1
]}

Huang Haoxuan ^{[1
]}

Deng Weisi ^{[2
]}

Yang Qiuling ^{[1
]}

机构：

[1] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430072, Peoples R China

[2] China Southern Power Grid, Power Dispatch & Control Ctr, Guangzhou 510663, Peoples R China

来源：

ENERGY REPORTS | 2023年 / 9卷

关键词：

Chaotic characteristics; Meteorological information; Phase space reconstruction; BiLSTM; NUMERICAL WEATHER PREDICTION; SUPPORT VECTOR MACHINES; TIME-SERIES MODELS; NEURAL-NETWORKS; SPEED; FORECAST; SIMULATE; DYNAMICS;

D O I：

10.1016/j.egyr.2023.04.288

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：

Aiming at the chaotic characteristics of wind power sequence and combined with meteorological information, a short-term prediction method of wind power based on phase space reconstruction and bidirectional long short-term memory neural network (Re-BiLSTM) is proposed. Firstly, the embedding dimension m and time delay tau of the time series are determined by the C-C method, and the wind power data is reconstructed based on the embedding theorem. The reconstructed data and normalized meteorological data (wind speed, wind direction) are then used as inputs, and bidirectional long short-term memory neural network (BiLSTM) is used to make short-term prediction of wind power. The results show that compared with artificial neural networks, BiLSTM, Random forest, and K-Nearest Neighbor, Re-BiLSTM has lower prediction error, which fully proves the effectiveness of the model. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

引用

页码：474 / 482

页数：9