Analytical Methodology of Monthly Residential Building Electricity Consumption Based on Data Mining Models. (February 2019)

• Record Type: Journal Article
• Authors: Kang, Xuyuan; Yan, Da; Sun, Hongsan; Jin, Yuan
• Is Part Of: IOP conference series. Volume 238(2019)
• Publication Date: 2019-02
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/238/1/012050
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: With the development of urbanization, the energy consumption of residential buildings is growing rapidly. Previous researches mainly focus on the daily profile of residential electricity consumption, while monthly electricity consumption profile is essential for the design and optimization of power system and energy storage system. With the advancement of smart meters as well as mass data storage and transfer techniques, the availability of building energy data is growing rapidly. Thus, data mining methods are becoming possible and effective in the analysis of monthly electricity consumption in residential buildings. This paper first reviews the current research of monthly energy consumption profiles in residential buildings, then proposes a new stochastic distribution method based on data-driven models. A case study of Jiangsu residential building electricity consumption is conducted to illustrate and verify the application of this method. Finally, this paper discussed the future promotion of this method and possible perspective of monthly energy profiles.