Research on Power Load Semi-supervised Clustering Based on Improved Ant Colony Algorithm

doi:10.12204/j.issn.1000-7229.2020.10.009

Abstract

Abstract:

The development of metrology communication technology enables the user’s load information to be accurately collected, and the clustering analysis of the power consumption characteristics of the load can be performed. In order to solve the problem that load clustering application scenarios need clustering results as similar as possible to initial cluster center, two improved ant colony clustering algorithms are designed on the basis of the ant colony clustering algorithm. The two factors that determine the clustering effect and the distance between the cluster center and the initial cluster center constitute the fitness index instead of the traditional mean square error for updating pheromone matrix. The example analysis shows that the algorithm can solve this kind of application scenario well and has good clustering effect.

Key words: power load, cluster analysis, ant colony algorithm, semi-supervised clustering

CLC Number:

TM714

DUAN Qingang, WANG Haohao, WU Zidong, WANG Yi, ZHU Tao, DONG Ping, LIU Mingbo. Research on Power Load Semi-supervised Clustering Based on Improved Ant Colony Algorithm[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(10): 81-89.

Figures/Tables 10

Fig.1 Calculation steps of improved ant colony clustering algorithm

Fig.2 SI of index improved ant colony clustering algorithms under different cluster numbers and ant numbers

Fig.3 DBI of index improved ant colony clustering algorithms under different cluster numbers and ant numbers

Fig.4 Clustering effect of SI and distance with different weights in SIp improved ant colony clustering algorithm

Fig.5 Clustering effect of DBI and distance with different weights in DBIp improved ant colony clustering algorithm

Fig.6 SI index comparison of the algorithms

Fig.7 DBI index comparison of the algorithms

Fig.8 Distance between clustering result and initial clustering center comparison of the algorithms

Table 1 Comparison of three indices of six algorithms

Fig.9 Initial three pretended clustering centers and clustering centers of the clustering algorithms

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