南方电网技术2025，Vol.19Issue(2)：1-9,9.DOI:10.13648/j.cnki.issn1674-0629.2025.02.001
基于2D-VMD和ConvLSTM的电力负荷图像化短期预测方法
A Short-Term Power Load Visualization Forecasting Method Based on 2D-VMD and ConvLSTM
摘要
Abstract
Power load forecasting is influenced by many uncertain events,so accurately predicting load has always been a key research direction in the industry.In response to the problem of low accuracy of traditional methods in short-term power load forecasting,a short-term power load visualization forecasting method is proposed based on two-dimensional variational mode decomposition(2D-VMD)and convolutional long short term memory neural networks(ConvLSTM).Firstly,the Gramian angular fields(GAF)method is used to convert the preprocessed load data into a set of Gram angular field images,and then the images are decomposed into a series of sub-modes with different center frequencies through 2D-VMD and classified according to the center frequencies.ConvLSTM neural network is used to predict the image groups with different modes.Finally,the prediction results are reconstructed and inversely operated to obtain the load prediction values.The prediction results indicate that this method improves the accuracy of short-term load forecasting and provides a new method for power load forecasting.关键词
电力系统/负荷预测/格拉姆角场/二维变分模态分解/ConvLSTM神经网络Key words
power system/load forecasting/Gramian angular field/two dimensional variational mode decomposition/ConvLSTM neural network分类
动力与电气工程引用本文复制引用
李承皓,杨永标,宋嘉启,张翔颖,徐青山..基于2D-VMD和ConvLSTM的电力负荷图像化短期预测方法[J].南方电网技术,2025,19(2):1-9,9.基金项目
国家重点研发计划资助项目(2022YFB2703500). Supported by the National Key Research and Development Program of China(2022YFB2703500). （2022YFB2703500）
