Method for Clustering Daily Load Curve Based on SVD-KICIC

Yikun Zhang, Jing Zhang, Gang Yao, Xiao Xu and Kewen Wei
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Yikun Zhang: School of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: School of Electrical Engineering, Guizhou University, Guiyang 550025, China
Gang Yao: Guizhou Power Grid Company, Guiyang 550001, China
Xiao Xu: School of Electrical Engineering, Guizhou University, Guiyang 550025, China
Kewen Wei: School of Electrical Engineering, Guizhou University, Guiyang 550025, China

Energies, 2020, vol. 13, issue 17, 1-15

Abstract: Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves.

Keywords: classification of load curves; singular value decomposition; dimensionality reduction; inter-class distance; weighted Euclidean distance; clustering validity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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