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Study of Residential Power Load Patterns Based on Clustering and Deep Belief Network

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DOI: 10.23977/jnca.2017.21002 | Downloads: 16 | Views: 1587

Author(s)

Wang Baoyi 1, Lv Jin 1, Zhang Shaomin 1

Affiliation(s)

1 School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China

Corresponding Author

Wang Baoyi

ABSTRACT

The study of power load patterns is the premise and basis of power distribution network maintenance. In view of the shortage of the existing power load model focusing on industry, agriculture, commerce and other large users, not on residents, In this paper, a method of residential power load patterns based on clustering and deep belief network is proposed. Firstly, use the improved k-means clustering algorithm for the residential electricity load clustering analysis to extract the typical load curve of each cell; and then a depth belief network classifier is constructed to classify the typical load curves of each cell, to identify the residential power load patterns and provide reliable support for distribution network maintenance. The effectiveness of the method is demonstrated by experiments on power data.

KEYWORDS

Residential Power, Load Patterns, Clustering, Classification

CITE THIS PAPER

Baoyi, W., Jin, L.,Shaomin, Z. (2017) Study of Residential Power Load Patterns Based on Clustering and Deep Belief Network. Journal of Network Computing and Applications (2017) Vol.2, Num. 1: 7-13.

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