摘要
准确地预测短期负荷为发电厂电力调度提供依据,提高电力系统的经济性。由于负荷数据的非线性非平稳性,提出一种经验模态分解-改进粒子群算法-长短期记忆(EMD-IPSO-LSTM)的预测模型。首先,利用EMD处理非线性的负荷序列,将序列分解为多个本征模态函数(IMF)以及残差(Res),引入非线性递减分配方法和正弦函数分别改进粒子群算法(PSO)的惯性权重和学习因子,可以更有效地寻找LSTM参数的最优解。其次,利用IPSO优化LSTM的第1层神经元个数、损失率、以及批量大小等参数,将所有IMF和Res分为高、中,低频三组分量,并代入优化后的LSTM网络进行预测,叠加获取最终的预测结果。最后,以GEFCom2014预测竞赛电力负荷数据集进行仿真实验,并且对LSTM、IPSO-LSTM、EMD-PSO-LSTM这3种模型作比较,结果表明所提的预测模型具有较高的预测精度。
Accurate prediction of short-term load provides basis for power dispatching of power plants and improves the economy of power systems.Due to the non-linear and non-stationary nature of load data,this paper proposes a prediction model based on empirical mode decomposition-improved particle swarm optimization-long short-term memory(EMD-IPSO-LSTM).First,the EMD is used to deal with the nonlinear load sequence,and the sequence is decomposed into multiple intrinsic mode functions(IMF)and residuals(Res).The nonlinear decreasing assignment method and sine function are introduced to improve the inertia weight and learning factor of particle swarm optimization(PSO)respectively,so that the optimal solution of LSTM parameters can be found more effectively.Secondly,the IPSO is used to optimize the parameters such as the number of neurons in the first layer of LSTM,the loss rate,and the batch size.All IMF and res are divided into three groups:high,medium,and low frequency components,and are substituted into the optimized LSTM network for prediction.The final prediction result is obtained by superposition.Finally,a simulation experiment is carried out with the GEFCom2014 power load forecasting competition data set,and compared with LSTM,IPSO-LSTM and EMD-PSO-LSTM.The results show that the proposed forecasting model has higher forecasting accuracy.
作者
赵一鸣
吉月辉
刘俊杰
陈嘉齐
Zhao Yiming;Ji Yuehui;Liu Junjie;Chen Jiaqi(School of Electrical Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China)
出处
《国外电子测量技术》
北大核心
2023年第1期132-137,共6页
Foreign Electronic Measurement Technology
作者简介
赵一鸣,硕士研究生,主要研究方向为电力系统负荷预测,E-mail:zhaoyiming_tjlg@126.com;通信作者:吉月辉,博士,副教授,主要研究方向为信息物理系统、电力系统负荷预测,E-mail:jiyuehuitju@126.com;刘俊杰,博士,讲师,主要研究方向为非线性系统建模与控制、电力系统负荷预测;陈嘉齐,硕士研究生,主要研究方向为电力系统负荷预测。
