软件导刊2024，Vol.23Issue(12)：10-17,8.DOI:10.11907/rjdk.232248
基于特征重组与IQPSO-BILSTM-RF的短期风电功率预测
Short-Term Wind Power Prediction Based on Feature Recombination and IQPSO-BILSTM-RF
摘要
Abstract
Short term wind power prediction is crucial for the normal operation of the power system.In order to improve the accuracy of wind power prediction,a combination model of bidirectional long short-term memory network(BILSTM)and random forest(RF)is proposed based on feature recombination method and improved quantum particle swarm optimization algorithm(IQPSO)to optimize the short-term wind power prediction.Firstly,using local mean decomposition to process wind power data,multiple sub components are obtained,and their fuzzy entro-py is calculated to recombine new feature components.Secondly,using IQPSO optimized BILSTM to predict feature components,the results of each component are superimposed to obtain preliminary predicted values.Finally,error correction was performed on the preliminary predicted values using IQPSO optimized RF.The experiment showed that the coefficient of determination(R2)of the model reached 0.994 25,which is superior to other models.The ablation experiment verified the necessity of each module.关键词
风电功率预测/特征重组/改进量子粒子群优化算法/双向长短期记忆网络/随机森林/误差修正Key words
wind power prediction/feature recombination/improved quantum particle swarm optimization algorithm/bidirectional long short-term memory network/random forest/error correction分类
信息技术与安全科学引用本文复制引用
王嘉琪,张玲华,胡枫..基于特征重组与IQPSO-BILSTM-RF的短期风电功率预测[J].软件导刊,2024,23(12):10-17,8.基金项目
国家自然科学基金项目(62371253) （62371253）
