Spatial Load Forecasting Method Based on Multiscale LDTW and TCN
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.
Spatial Load Forecasting(SLF)Dynamic Time Warping(DTW)spectral clusteringsimultaneous rateTemporal Convolutional Network(TCN)
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