High-resolution Load Profile Clustering Approach Based on Dynamic Largest Triangle Three Buckets and Multiscale Dynamic Warping Path Under Limited Warping Path Length  

作　　者：Mi Wen Yue Ma Weina Zhang Yingjie Tian Yanfei Wang 

机构地区：[1]College of Computer Science and Technology,Shanghai University of Electric Power,Shanghai,China [2]Shanghai Electric Power Research Institute,Shanghai,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2023年第5期1576-1584,共9页现代电力系统与清洁能源学报（英文）

基　　金：supported by the Joint Fund of National Natural Science Foundation of China (No. U1936213);National Natural Science Foundation of China (No. 61872230);Program of Shanghai Academic Research Leader (No. 21XD1421500);Shanghai Science and Technology Commission Project (No. 20020500600)。

摘　　要：With the popularity of smart meters and the growing availability of high-resolution load data, the research on the dynamics of electricity consumption at finely resolved timescales has become increasingly popular. Many existing algorithms underperform when clustering load profiles contain a large number of feature points. In addition, it is difficult to accurately describe the similarity of profile shapes when load sequences have large fluctuations, leading to inaccurate clustering results. To this end, this paper proposes a high-resolution load profile clustering approach based on dynamic largest triangle three buckets(LTTBs) and multiscale dynamic time warping under limited warping path length(LDTW). Dynamic LTTB is a novel dimensionality reduction algorithm based on LTTB. New sequences are constructed by dynamically dividing the intervals of significant feature points. The extraction of fluctuation characteristics is optimized. New curves with more concentrated features will be applied to the subsequent clustering. The proposed multiscale LDTW is used to generate a similarity matrix for spectral clustering, providing a more comprehensive and flexible matching method to characterize the similarity of load profiles. Thus, the clustering effect of a high-resolution load profile is improved. The proposed approach has been applied to multiple datasets. Experiment results demonstrate that the proposed approach significantly improves the Davies-Bouldin indicator(DBI) and validity index(VI). Therefore, better similarity and accuracy can be achieved using high-resolution load profile clustering.

关 键 词：Load profile clustering largest triangle three buckets(LTTB) dynamic time warping(DTW) spectral clustering 

分 类 号：TM933.4[电气工程—电力电子与电力传动]