## 1 Introduction  
With increasing urbanization, research in building energy efficiency is becoming more important on a global basis. In 2018, total building energy consumption was 899 million tons of standard coal equivalent (tce), accounting for 20.62% of the total energy consumption in China. In addition, public building energy consumption was 346 million tce, accounting for 38.53% of the total building energy consumption [1; 2]. Thus, energy efficiency in public buildings has become a necessity to study the energy-saving potential of different sub-items and energy efficiency strategies. Scholars from various countries study the electricity consumption characteristics of public buildings with different functions. [PERSON] et al. found that the ratio of electricity consumption for air-conditioning, office equipment, elevators, and lighting sockets in commercial buildings was 43%, 17%, 7%, and 34%, respectively, according to an investigation of 16,000 commercial buildings in Hong Kong [3; 4; 5]. These results indicate that air-conditioning and lighting sockets in public buildings play an important role in building energy consumption, of which operation characteristics are worthy of special attention. [PERSON] et al. used various machine learning technique classifiers to analyze and classify building energy efficiency, and they demonstrated that reliable classification is feasible with a few featured parameters [6]. [PERSON] et al. developed a methodology of energy efficiency for lighting and air-conditioning systems in buildings using a multi-objective optimization algorithm [7]. In recent years, a large number of studies have focused on the study of human behavior models, because occupant behavior is complex and requires an interdisciplinary approach to be fully understood. On the one hand, occupant behavior is influenced by external factors such as culture, economy, and climate, as well as internal factors such as individual comfort preference, physiology, and psychology. On the other hand, the occupants' interactions with building systems strongly influence building operations and thus energy use/cost and indoor comfort; this in turn influences occupant behavior, thus forming a closed loop. Therefore, in the study of building energy consumption, it is difficult to avoid the study of human behavior in buildings. For example, [PERSON] et al. clustered research keywords on human behavior and presented them with visual images, pointing out the close relationship among building energy consumption, human behavior, and environmental comfort [8]. [PERSON] et al. studied the influence of human behavior in the construction of a low-carbon campus in Shanghai, and the results showed that individual behavior directly affected the overall energy consumption and carbon emissions of the campus. Although the relevant energy-saving policies were introduced, energy consumption still increased by 5% per year [9]. [PERSON] et al. simulated human behavior in buildings and pointed out that architectural design should be optimized according to the actual human behavior and architectural characteristics for special buildings, and the description of human behavior should be more detailed [10]. [PERSON] et al. analyzed the Monte Carlo sensitivity analysis results of factors affecting annual heating energy consumption and predicted mean vote (PMV) comfort index (related to human behavior) in Northern Europe [11]. [PERSON] presented the environment- and event-related driving forces behind lighting usage in office buildings, described the degree to which these factors influence lighting use, and used a function and probability relations to describe the random process of lighting use [12]. The problem of energy consumption data quality cannot be ignored. In fact, in other related fields, some scholars have also conducted relevant research, for example, [PERSON] et al. developed an iterative online fault identification framework utilizing a novel lost data repair technique [13].