电力需求侧管理2024，Vol.26Issue(1)：48-53,6.DOI:10.3969/j.issn.1009-1831.2024.01.008
基于改进PSO优化LSTM网络的典型用电负荷模式识别
Typical power load mode recognition based on IPSO optimization and LSTM network
摘要
Abstract
Promotion and use of new energy power generation has aggravated the contradiction between supply and demand of power grid during peak hours.Identification of load patterns of power users can provide support for load participation in peak regulation decisions.In order to improve the accuracy of power load pattern recognition,a power load pattern recognition model based on improved particle swarm optimization(IPSO)algorithm to optimize long short-term memory(LSTM)neural network is proposed.By introducing diversified initial pa-rameters,dynamic nonlinear weights and elimination mechanism,the optimization ability of PSO algorithm is improved,the key parame-ters of LSTM are optimized,and optimal parameter combination of LSTM neural network is determined.Experimental results show that this method can effectively improve the accuracy of the model and save the training time of the model.关键词
负荷模式/改进粒子群算法/长短期记忆神经网络/参数寻优Key words
load mode/improved particle swarm optimization/long short-term memory neural network/parameter optimization分类
能源科技引用本文复制引用
贾磊,龚正,吴海伟,耿文逸,王琚玮..基于改进PSO优化LSTM网络的典型用电负荷模式识别[J].电力需求侧管理,2024,26(1):48-53,6.基金项目
国网江苏省电力有限公司科技项目(J2022010) （J2022010）
