Short-TermWind Power Prediction Based on Combinatorial Neural Networks

作     者：Tusongjiang Kari Sun Guoliang Lei Kesong Ma Xiaojing Wu Xian 

作者机构：School of Electrical EngineeringXinjiang UniversityUrumqi830017China Anhui Nari Jiyuan Electric Power System Tech Co.LTDHefei230088China 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第8期

页      面：1437-1452页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：support of national natural science foundation of China(No.52067021) natural science foundation of Xinjiang(2022D01C35) excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012) major science&technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2) 

主　　题：Wind power prediction wavelet transform back propagation neural network bi-directional long short term memory 

摘      要：Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid *** wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid *** the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is ***,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the ***,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind ***,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.