## 1 Introduction
This algorithm can adjust the estimation results according to the error size and can be used to separate the electrical energy consumption of multiple buildings in the same power grid, which overcomes some disadvantages of the NILM method. Newer studies, such as that of [PERSON] et al., used the outlier detection method to eliminate abnormal energy consumption data and then used canonical variable analysis to classify and predict energy consumption [20]. [PERSON] proposed an energy consumption separation algorithm for an equipment load rate model. The estimation results of the equipment power consumption were obtained by inputting the rated power of the equipment, which means that the equipment power consumption is separated from total power consumption [21]. [PERSON] proposed a socket power consumption model, which was developed by comparing power consumption data and the corresponding equipment information. This model is applicable in separating various types of power consumption in sockets, but it is difficult to separate the air-conditioning power consumption, which is mixed with socket power consumption [22]. [PERSON] proposed a new parameter, energy consumption per capita (\(K\)), to explain the stochastic relationship between energy consumption and utilization, which was developed by multiple nonlinear regression and deep neural network algorithms. This model can be used to estimate the energy consumption of different sub-items according to human behavior, which means that the sub-item power consumption is separated from total power consumption [23]. However, the common point of these energy separation methods [24; 25; 26; 27; 28; 29; 30] is to obtain the actual energy consumption data or to estimate the energy consumption of some sub-items according to the detailed information of buildings, equipment, and human behavior. The energy consumption data of different equipment can then be calculated, which means that this type of equipment power consumption is separated from total power consumption. If detailed information on buildings, equipment, and human behavior cannot be obtained, or is for public buildings, of which equipment and human behavior are complex, these methods still have some defects. From the literature [1; 2; 3; 4; 5; 6; 7], we can discover the necessity of building energy efficiency, especially for public buildings. Therefore, how to use energy consumption data in monitoring platform is important, such as studying human behavior for an energy retrofit shown in the literature [8; 9; 10; 11; 12; 13]. This means that accurate itemization measurement is important in the research of public building energy efficiency. However, in the face of various practical problems, it is often difficult to make accurate itemization measurement. Therefore, researchers have developed various algorithms to repair energy consumption data, such as those found in the literature [16; 17; 18; 19; 20; 21; 22; 23], but most of the above separation methods rely on the detailed information of buildings, equipment, and human behavior. It is usually time-consuming and inefficient to survey this information. Thus, under the condition of generally abnormal energy consumption monitoring data and high labor cost, research on separation methodology, which is driven only by historical data and applied online with a universality of multiple scenarios without detailed information of buildings and equipment, is critical. Therefore, this study proposes an online separation methodology for separating the power consumption of lighting sockets and air-conditioning in public buildings. This methodology is driven only by historical data, without detailed information of buildings and equipment. The rest of this paper is organized as follows. Section 2 introduces the basic information in this research, including the sample campus, building energy monitoring platform, and actual problems with data quality with two aspects. Section 3 introduces methods of pre-processing power consumption data with the k-means clustering algorithm.