Title: Research on residents' electricity behaviour analysis and control strategy optimisation

Authors: Yaxuan Chen; Xiaobin Cheng; Kai Huang; Jinpeng Liu; Tao Yi

Addresses: School of Economics and Management, North China Electric Power University, No. 2, Beinong Rd., Huilongguan Town, Changping District, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, No. 2, Beinong Rd., Huilongguan Town, Changping District, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, No. 2, Beinong Rd., Huilongguan Town, Changping District, Beijing 102206, China ' Beijing Key Laboratory of New Energy and Low-Carbon Development, School of Economics and Management, North China Electric Power University, No. 2, Beinong Rd., Huilongguan Town, Changping District, Beijing 102206, China ' School of Economics and Management, North China Electric Power University, No. 2, Beinong Rd., Huilongguan Town, Changping District, Beijing 102206, China

Abstract: In recent years, the electricity consumption of residents has continued to grow, which has brought impact to global climate, it is of great significance to dig deep into the characteristics of residential electricity consumption. This paper introduces the ReliefF algorithm to reduce the load characteristic index. The resident electricity behaviour analysis model based on DBSCAN algorithm is further constructed to analyse the index data, and then the empirical calculation and data mining analysis are carried out. On account of the clustering results, the judgment radar matrix of user control attribute is advanced to clarify differentiated regulation strategies and meticulous regulative strategies for various residents are proposed. The analysis model introduced in this paper can accurately describe the user's electricity behaviour characteristics, so that the proposed differential control strategy can improve the demand side management level, which will alleviate global warming to some extent.

Keywords: residential electricity consumption behaviour; ReliefF algorithm; DBSCAN algorithm; cluster analysis; regulation strategy.

DOI: 10.1504/IJGW.2020.110214

International Journal of Global Warming, 2020 Vol.22 No.1, pp.91 - 110

Received: 02 Jan 2020
Accepted: 30 Mar 2020
Published online: 09 Oct 2020 *

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