Data mining based framework for exploring household electricity consumption patterns : A case study in China context

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)773-785
Journal / PublicationJournal of Cleaner Production
Volume195
Early online date30 May 2018
Publication statusPublished - 10 Sep 2018

Abstract

This study proposes a data mining based framework for exploring the electricity consumption patterns, which includes three consecutive stages. Firstly, electricity consumption patterns and behaviors are explored in festivals such as the Spring Festival, the Labor Day and the National Day. Secondly, seasonal electricity consumption patterns and behaviors are compared, and the relationship between temperature and electricity demand is analyzed through data visualization. Thirdly, we focus on the phenomenon of electricity consumption patterns shifting. Finally, a case study of Nanjing and Yancheng City, Jiangsu Province, China is presented. The results indicate that: (1) Volatility of electricity consumption is higher in winter and summer than in spring and autumn. (2) There are three typical load profiles during the Spring Festival, two typical load profiles during the Labor Day the National Day. (3) High temperature in summer and low temperature in winter have obvious influence on electricity consumption. However, the electricity consumption peak lags one or two days behind the temperature peak in summer, and consumers’ response time gets shorter as the frequency of temperature peaks increase. (4) The phenomenon of instability of household electricity consumption patterns is identified. 7.22% of the high volatility households transferred to low volatility households from winter to spring. 6.08% low volatility households transferred to high volatility households from summer to autumn. Finally, we proposed some suggestions for promoting energy conservation and improving energy efficiency.

Research Area(s)

  • Clustering, Framework, Household electricity consumption patterns, Seasonal characteristics, Temperature

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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