Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

作     者：Jing Gao Mingxuan Ji Hongjiang Wang Zhongxiao Du 

作者机构：School of Electric PowerShenyang Institute of EngineeringShenyang100083China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第6期

页      面：5017-5030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：funded by Liaoning Provincial Department of Science and Technology(2023JH2/101600058) 

主　　题：Short-term wind power prediction deep hybrid kernel extreme learning machine incremental learning error clustering 

摘      要：With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutralityprocess,the proportion of wind power is *** the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is *** algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM ***,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training ***,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction *** the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power *** examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration *** accuracy and precision of wind power generation prediction are effectively improved through the method.