计算机工程2024，Vol.50Issue(3)：106-113,8.DOI:10.19678/j.issn.1000-3428.0066944
基于多尺度LDTW和TCN的空间负荷预测方法
Spatial Load Forecasting Method Based on Multiscale LDTW and TCN
摘要
Abstract
Spatial Load Forecasting(SLF)provides necessary guidance for the rational construction and use of substations and feeders,and it has become an indispensable aspect of distribution network planning.The refinement of distribution network planning has generated a large number of high-resolution load data,and the rapid development of society has made the electricity characteristics of land plots increasingly complex.The current spatial load-forecasting method does not fully consider the time characteristics between load data and ignores the possible inconsistent time of the peak load between different types of blocks during the forecasting process.The proposed spatial load-forecasting method analyzes the similarity of users'load profiles in shape by spectral clustering based on multiscale LDTW and extracts the typical electricity consumption behaviors of different plots.Based on further classification,the corresponding simultaneity rate of plots of the same type is determined.Multiscale LDTW can inhibit pathological alignment by limiting the upper limit of matching steps between sequences and improve the comprehensive evaluation ability of curve similarity.Based on the clustering results,the training samples suitable for the region to be predicted are screened,and the regression forecasting model based on the Temporal Convolutional Network(TCN)is established.The forecasting results are aggregated based on the simultaneity rates of the blocks to achieve spatial load forecasting.The experimental results show that the proposed method strengthens the analysis of the shape of the load curve and distinguishes the simultaneity rate of different types of blocks.In terms of clustering,the DBI index reaches 0.57,and the VI index reaches 0.31.In terms of forecasting,the relative error reaches up to 1.93%,and the coefficient of determination is up to 0.941,which indicates a significant improvement compared with other methods.关键词
空间负荷预测/动态时间规整/谱聚类/同时率/时间卷积网络Key words
Spatial Load Forecasting(SLF)/Dynamic Time Warping(DTW)/spectral clustering/simultaneous rate/Temporal Convolutional Network(TCN)分类
信息技术与安全科学引用本文复制引用
马越,温蜜..基于多尺度LDTW和TCN的空间负荷预测方法[J].计算机工程,2024,50(3):106-113,8.基金项目
国家自然科学基金(U1936213). （U1936213）
