中国电力2024，Vol.57Issue(6)：121-130,10.DOI:10.11930/j.issn.1004-9649.202306104
基于DTW K-medoids与VMD-多分支神经网络的多用户短期负荷预测
Short-term Load Forecasting Based on DTW K-medoids and VMD Multi-branch Neural Network for Multiple Users
摘要
Abstract
Multi-user power load forecasting refers to the power load forecasting of multiple users or regions based on historical loads data,which can make the grid companies understand the power demands of different users or regions,so as to better carry out the planning and scheduling optimization of the power system.However,different users have complex and diverse power consumption behaviors,so it is difficult to use traditional methods to universally model different power users'loads and achieve accurate prediction.Therefore,a new multi-user short-term load prediction model based on DTW K-medoids and VMD-multi-branch neural network is established.Firstly,in order to improve the clustering performance of traditional clustering methods,the DTW K-medoids method is used to cluster users'load data,and the distance between loads data is calculated using the dynamic time warping(DTW)instead of the traditional Euclidean distance measurement method in K-medoids to improve the clustering effects of multiple users'load.Secondly,in order to fully characterize the long short-term time series-dependent characteristics of load history data,a parallel load forecasting method based on VMD-multi-branch neutral network model is established for multi-user short-term load forecasting.Finally,the 365-day load data of 20 users in a region is used for clustering,training and experiment,and the results show that the MAE and RMSE indexes of the proposed model significantly decrease compared with that of the comparative models,indicating that the proposed method can effectively characterize the power consumption behaviors of multiple users and improve the prediction efficiency and accuracy of multi-user loads.关键词
多用户/负荷预测/DTW K-medoids聚类/变分模态分解(VMD)/多分支神经网络Key words
multi-user/load forecasting/DTW K-medoids clustering/variational mode decomposition/multi-branch neural network引用本文复制引用
王宇飞,杜桐,边伟国,张钊,刘慧婷,杨丽君..基于DTW K-medoids与VMD-多分支神经网络的多用户短期负荷预测[J].中国电力,2024,57(6):121-130,10.基金项目
国网冀北张家口供电公司2022年群众性创新项目(B30107220006) （B30107220006）
河北省自然科学基金资助项目(E2021203004). This work is supported by 2022 Mass Innovation Project of State Grid Zhangjiakou Power Supply Company(No.B30107220006)and Natural Science Foundation of Hebei Province(No.E2021203004). （E2021203004）
