A density-based matrix transformation clustering method for electrical load

Naiwen Li, Xian Wu, Jianjun Dong, Dan Zhang and Shuai Gao

PLOS ONE, 2022, vol. 17, issue 8, 1-15

Abstract: Feature extraction of electrical load plays a vital role in providing a reliable basis and guidance for power companies. In this paper, we propose a novel clustering algorithm named the Density-based Matrix Transformation (DBMT) Clustering method to extract features (peaks, valleys and trends) of electrical load curves. The main objective of the algorithm is to reorder the data items until the data items belonging to the same cluster are organized together; that is, the adjacent matrix is rearranged to the type of block diagonal. This method adaptively determines the number of clusters and filters out noise without input global parameters. Moreover, for the specific characteristics of raw electrical load data, we propose a variant of Dynamic Time Warp (DTW) distance, dsDTW, which aligns the peaks, valleys and trends of load curves meanwhile dealing with missing values in different situations. After feeding the dsDTW adjacent matrix to DBMT, the results indicate that our proposal can accurately extract the feature of the load curves compared to different clustering methods.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0272767

DOI: 10.1371/journal.pone.0272767

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