## 1 Introduction
To obtain the energy consumption monitoring data for guiding the research about human behavior and energy consumption, it is necessary to accurately obtain the lighting power consumption and socket power consumption, which have an obvious correlation with human behavior, as shown in Figure 1. It is worth noting that most public buildings in cold and severely cold regions of China use municipal hot water for central heating, which is charged by area rather than volume used actually and does not consume electricity from lighting sockets. It means that each room in winter does not consume electricity from lighting sockets to create a thermal environment, and the environment is created using the radiator with the hot water prepared centrally. This means that the research about separation methodology focuses not on energy consumption of heating but on electricity consumption. In the past decade, the Chinese government supported the establishment of an energy monitoring platform to monitor and analyze energy consumption data online, especially electricity consumption. The energy monitoring platform has become an important technical carrier for building energy efficiency [14; 15], but in most of the existing building energy monitoring platforms, these two items of power consumption data are often difficult to obtain. First, due to cost constraints and construction age, the construction process of the building electrical system is not designed according to the correct design of electrical items. The monitoring branches of lighting, sockets, and air-conditioning, shown in red in Figure 2, are mixed. Second, due to the changes in the functions and partitions of a building, the distribution circuit scheme also changes accordingly. In this case, even if the lighting is separated from sockets in the design scheme, the air-conditioning consumption is often mixed into the socket consumption in the renovation process. So, especially for public buildings, it is difficult to retrofit electric circuits massively, and at the same time, the aforementioned problems arise when the room function or room user changes. As a result, with this background, the itemized energy consumption data, which we obtain using an electrical loop monitoring instrument, will be inaccurate, because we fail to completely separate the power consumption of the lighting, socket, and air-conditioning terminal. Taking the study of human behavior as an example, if the power consumption of lighting sockets and air-conditioning cannot be accurately separated, it is difficult to evaluate the role of human behavior, outdoor weather, and envelope structure. Therefore, how to separate the mixed power consumption of lighting sockets and air-conditioning without large-scale reconstruction is worthy of investigation. Scholars have presented a type of non-intrusive load monitoring (NILM) method. By measuring the curve of real-time power consumption, the start and stop electrical signals (current, voltage, active power, reactive power, power factor, etc.) of the equipment can be identified  
Figure 1: The indoor energy consumption component and its influencing factors in public buildings. Figure 2: Construction drawing of electric energy monitoring for a sample building studied in this paper. for abnormal power consumption data identification and separation [16; 17; 18]. However, these relevant studies mainly focus on residential buildings with a few types of equipment. A possible reason for this is that the types of equipment in public buildings are complicated and their number is extremely large. Thus, it is extremely difficult to judge the state of equipment only by the total energy consumption curve. Therefore, [PERSON] [19] developed an end-use disaggregation algorithm based on the hybrid technology of simulation software and statistical analysis.