Load Clustering Method Based on Improved Density Peak and Gaussian Mixture Model. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Zhe, Dong; Ya, Wang; Chuanxiao, Ma; Weihan, Ai
• Is Part Of: Journal of physics. Volume 2171:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2171/1/012035
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: A load clustering method based on improved density peak and Gaussian mixture was proposed to solve the problems that standard Gaussian mixture clustering algorithm (GMM) with random initial clustering center was easy to fall into local optimum in the clustering analysis of residential electricity behavior, and the density peak algorithm (DPC) needed to manually select the clustering center.The method combines the Cosine distance and Euclidean distance, which reflect the similarity of curve shape, and selects the clustering center automatically through the sine value relation of relative Angle between two points in γ descending graph. The clustering center selected by the improved DPC algorithm was used to initialize the parameters of the GMM model. Finally, load clustering is carried out by GMM model. The results of calculation examples show that the proposed method can effectively improve the efficiency of daily load curve clustering.