一种基于改进VMD-PSO-CNN-LSTM的短期电价预测方法  

A short-term electricity price forecasting method based on improved VMD-PSO-CNN-LSTM

作　　者：郭雪丽 华大鹏 包鹏宇 李婷婷 姚楠 曹艳 王莹 张天东 胡钋[3] GUO Xueli;HUA Dapeng;BAO Pengyu;LI Tingting;YAO Nan;CAO Yan;WANG Ying;ZHANG Tiandong;HU Po(Nanyang Power Supply Company,State Grid Henan Electric Power Company,Nanyang 473000,China;Hubei Intelligent Cloud Network Dispatching and Operation Center,China Telecom,Wuhan 430022,China;School of Electrical Engineering and Automation,Wuhan University,Wuhan 430072,China)

机构地区：[1]国网河南省电力公司南阳供电公司,河南南阳473000 [2]中国电信股份有限公司湖北智能云网调度运营中心,湖北武汉430022 [3]武汉大学电气与自动化学院,湖北武汉430072

出　　处：《电力科学与技术学报》2024年第2期35-43,共9页Journal of Electric Power Science And Technology

基　　金：国家自然科学基金(51977160);国网河南省电力公司科技项目(SGHANY00CTJS220475)。

摘　　要：为了提升电价预测的准确性和预测模型的稳定性,提出一种基于改进VMD-PSO-CNN-LSTM的短期电价预测方法。首先,通过研究变分模态分解(variational mode decomposition,VMD)与电价影响因素的相关影响程度,并引入最大信息系数(MIC)构建VMD参数优化模型;然后,利用卷积神经网络(convolutional neural networks,CNN)与长短期记忆(long short-term memory,LSTM)神经网络对VMD分解得到的各模态分量进行预测。同时,根据深度可分离卷积结合电价时间规律,在CNN卷积部分构建多尺度的卷积特征提取结构,并利用粒子群优化算法优化包括CNN卷积层数量、CNN卷积神经元数量、LSTM隐藏层数量、LSTM记忆时间以及全连接层数等在内的参数,从而实现模型预测准确性和稳定性的提升。最后,对澳洲电力市场日前电价进行分析预测并与对照算法对比,结果表明该文算法具有更高的精度和更好的稳定性。To improve the accuracy of electricity price forecasting and the stability of forecasting models,a short-term electricity price forecasting method based on improved VMD-PSO-CNN-LSTM is proposed.Firstly,after studying the correlation between variational mode decomposition(VMD)and the influencing factors of electricity prices,and introducing the maximum information coefficient,a parameter optimization model for VMD is constructed.Secondly,convolutional neural networks(CNN)and long short-term memory(LSTM)neural networks are used to predict the modal components obtained by VMD decomposition.As for the convolution in CNN,a extraction structure with multi-scale convolution feature is constructed,on the basis of the depth-wise separable convolution combined with the time law of electricity prices.Particle swarm optimization algorithm is then used to optimize parameters including the number of CNN convolutional layers,the number of CNN convolutional neurons,the number of LSTM hidden layers,LSTM memory time,and the number of fully connected layers,so as to improve the prediction accuracy and stability of the model.Finally,the analysis and prediction of the day-ahead electricity prices in the Australian electricity market are carried out and compared with the algorithm.The results show that the proposed algorithm has higher accuracy and better stability.

关 键 词：电价预测 变分模态分解 粒子群优化算法 卷积神经网络 长短时间记忆神经网络 

分 类 号：TM732[电气工程—电力系统及自动化]