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With the exponential development of Chinese population, the massive energy consumption of buildings has recently become an interest subject. Although much research has been conducted on residential buildings, heating ventilation and air conditioning (HVAC), little research has been conducted on the relationship between student’s behavior, campus buildings, and their subsystems. Using classical seasonal decomposition, hierarchical clustering, and apriori algorithm, this paper aims to provide an empirical model for consumption data in campus library. Smart meter data from a library in Beijing, China, is adopted in this paper. Building electricity consumption patterns are investigated on an hourly/daily/monthly basis. According to the monthly analysis, electricity consumption peaks each year around June and December due to teaching programs, social exams, and outdoor temperatures. Hourly data analysis revealed a relatively stable consumption pattern. It shows three different types of daily load profiles. Daily data analysis demonstrated a high relationship between HVAC consumption and building total consumption, with a lift value of 5.9. Furthermore, links between temperature and subsystems were also discovered. Through a case study of library, this study provides a unique insight into campus electricity use. The results could help to develop operational strategies for campus facilities.

Research on Electricity Consumption Model of Library Building Based on Data Mining