An Improved Spectral Clustering Algorithm Using Fast Dynamic Time Warping for Power Load Curve Analysis

Bi, Zhongqin; Leng, Yabin; Liu, Zhe; Li, Yongbin; Fuentes, Stelios

doi:10.1007/978-3-030-64214-3_10

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…Numerous methods, including K-medoids [6], K-means [7], fuzzy C-means [8], and spectral clustering [9], [10], have been used for load profile clustering. However, the popular advanced monitoring systems in power distribution networks provide more details related to the power consumption behavior and complicate the load profile analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, DTW has been used in cluster analyses of load profiles [15], [16] to provide a more accurate evaluation of the similarity based on the shape of a curve. Reference [10] used improved fast DTW to calculate coarse-grained data to form a similarity matrix of spectral clustering. Although the calculation efficiency was improved, the accuracy was decreased.…”

Section: Introductionmentioning

confidence: 99%

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

Wen,

Ma,

Zhang

et al. 2023

Journal of Modern Power Systems and Clean Energy

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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.

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