Data-driven correlation model between human behavior and energy consumption for college teaching buildings in cold regions of China. (June 2021)

• Record Type: Journal Article
• Authors: Zhao, Tianyi; Zhang, Chengyu; Xu, Jingwen; Wu, Yifan; Ma, Liangdong
• Is Part Of: Journal of building engineering. Volume 38(2021)
• Publication Date: 2021-06
• Subjects: Building energy monitoring system -- Law of human behavior -- Data-based model -- Building energy efficiency -- Level coefficient -- Teaching buildings; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2020.102093
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Correlation models between human behavior and energy consumption of teaching buildings are crucial for devising energy efficiency strategies. Especially in teaching buildings in campus, on one hand, the investment is limited and the level of operation and maintenance is low. On the other hand, the certain management system exists, and the flow of occupant in campus is also regular obviously. So, it is difficult for large-scale technical retrofits and possible for management retrofits. In order to the low-cost management retrofits, the model between human behavior and energy consumption are studied in this paper. According to the models, on the one hand, managers of campus can predict energy consumption based in historical, for matching energy consumption supply and demand each other. On the other hand, managers can judge future human behaviors based on historical energy consumption, for conducting management to reduce energy consumption. In this paper, first, correlation models between the energy consumption and human behavior are proposed and the lighting management level coefficient K is defined to evaluate the lighting management level. With the aid of the correlation models and K, managers can obtain management strategies to increase energy efficiency without large-scale technical retrofits. The verification results show that the proposed strategies could bring a 13.96% economic benefit ratio. Second, correlation models between human behavior and influencing factors of human behavior are proposed for predicting human behavior. Finally, a technical route of energy efficiency retrofit by management is suggested and validated. Although the energy-saving ratio is not conspicuous, the cost is low and the operation is easy. It means that the technical route is worthy for research and promotion. Highlights: Correlation models are built to forecast consequences of human behavior and electric consumption. Evaluation indexes are defined for measuring the energy management level.