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Buildings are one of the world’s largest energy consumers and carbon emission producers. According to the tracking report from the International Energy Agency (IEA), the operation of buildings accounted for about 30% of global final energy consumption and 27% of total energy system emission in 2021. About 19% of energy-related carbon emission came from the generation of electricity and heat used in buildings. Compared to 2010, electricity consumption increased by 30% in 2021 [1]. With the widespread use of central heating, ventilation and air conditioning (HVAC) systems, the energy consumption per unit area of public buildings in China increased from 17 kgce/m2 in 2001 to more than 24.7 kgce/m2 in 2020 [2]. Various energy conservation measures need to be adopted to improve building energy efficiency and reduce carbon emission, which increases the possibility of achieving carbon neutrality in China by 2060.

At the building operation stage, accurate building system operation data are indispensable for optimizing HVAC system control strategy and analyzing building energy-saving potential. Therefore, it is very important to obtain accurate building energy consumption data, especially data of HVAC units.

1.1. The Status Quo of Sub-Metering Systems

In recent years, energy sub-metering systems have been widely implemented in various buildings. A majority of electrical systems and equipment can be directly monitored and metered. Electricity sub-metering was first proposed by Dr. Hart in 1992 [3]. In 2008, the California Public Utilities Commission (CPUC) adopted a resolution to carry out electricity sub-metering in multi-story commercial buildings [4]. In 2007, the Chinese government issued document No. 558, declaring that a special compensation would be used for building energy monitoring platforms [5]. Large-scale investigations on public building energy consumption have been conducted in Shanghai since 1995 and have then extended to other cities in China, including Beijing, Tianjin, Changsha and Wuhan [6,7,8]. Statistical data from Shanghai show that more than 1400 large public buildings (the total area exceeding 60 million m2) have installed a sub-metering system [9].The technical guidelines for electricity sub-metering data collection in China’s document No. 114 clearly stipulate that an entire building’s electricity consumption should be recorded by four primary sub-metering circuits, including the lighting-plug circuit, HVAC circuit, power circuit and other circuit [10]. This electricity sub-metering classification diagram is illustrated in Figure 1. The technical guidelines also point out that the electricity consumption of HVAC terminal units (e.g., fan coils and variable air volume (VAV) boxes) can be recorded in the lighting-plug circuit because, based on existing electricity-supply circuit design, HVAC terminals are usually plugged in the lighting-plug circuit [10]. Therefore, there is a grey area between an actual sub-metering model (black lines) and the theoretical (red lines) sub-metering classification model (shown in Figure 1). Additionally, the HVAC electricity consumption obtained from a building sub-metering system would be lower than the actual HVAC electricity consumption. In China, this is a common phenomenon. We conducted a survey of about 1400 buildings with a sub-metering system in Shanghai and found that only two buildings had direct sub-meters of HVAC terminal units. In most buildings, it is still impossible to obtain the electricity consumption of HVAC terminal units directly.However, accurate sub-metering data are extremely significant in many fields, including evaluating the energy-saving potential of buildings [11], selecting suitable energy efficiency technology [12], reducing carbon emissions of building operation [13], and securing the optimal control of HVAC systems [14]. Therefore, it is necessary to establish an indirect method to acquire the electricity consumption of HVAC terminal units.

1.2. Non-Intrusive Load Monitoring (NILM) Method

In the early 1990s, researchers introduced a method called non-intrusive load monitoring (NILM) to measure the electricity consumption of facilities in buildings [15,16,17,18]. This method can also be used to distinguish equipment’s on/off signals (e.g., current, voltage, active power and reactive power) and record the power and operational duration. This method is eminently suitable for residential buildings with low-power appliances and simple electricity supply systems [19,20]. Norford et al. applied a NILM method to small-scale commercial buildings [21]. They proposed a load disaggregation model to distinguish the load of air-conditioning systems from other electrical appliances with similar power levels or operation time [22].NILM methods can be divided into four categories: machine learning method, sparse coding, dynamic time warping (DTW) and Fourier analysis. Machine learning is one of the most popular approaches because it is easy to use and has high accuracy. Rafsanjani et al. combined Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and quadratic discriminant analysis (QDA) to disaggregate total building energy loads into individual energy consumption [23,24]. Kaselimi et al. proposed a Bayesian-optimized Bidirectional LSTM regression model to disaggregate the energy consumption of residential buildings [25]. Schirmer et al. used feed-forward Deep Neural Networks (DNNs), k-Nearest Neighbors (KNNs), Random Forests (RFs) and Support Vector Machines (SVMs) to disaggregate the electricity consumption of residential buildings separately [26]. Kaselimi M et al. proposed a Convolutional Neural Network (CNN)-based structure to estimate the current state of appliances in residential buildings [27]. Yang D et al. proposed an event-driven NILM method based on CNN to extract the energy consumption behavior of occupants [28]. Faustine A et al. proposed a UNet-NILM method based on multi-label learning strategy and multi-target quantile regression to detect the state and power of electrical appliances [29]. Xia et al. presented a deep Long Short-Term Memory (LSTM) model for load disaggregation. In this model, an encoder–decoder structure was designed to solve the problem of time dependency in multi-state devices [30]. Guo et al. presented a load disaggregation method based on combinatorial optimization to disaggregate the electricity load of residential buildings [31]. Monteiro et al. disaggregated residential buildings’ electricity load by using Multi-layer Perceptron (MLP), LSTM and Convolutional Neural Network (CNN) [32]. Samadi and Fattahi used K-means method to cluster the daily load profiles of buildings and calculated the common load and occupancy load in different day types [33]. Xiao et al. applied RF for the cooling load disaggregation of office buildings [34]. Athanasiadis C et al. proposed a real-time NILM method, including an event detection algorithm, a CNN classifier and a power estimation algorithm, to estimate the energy consumption of household appliances [35]. Shao et al. proposed a load disaggregation approach based on temporal motif mining. This approach used a clustering method to detect the power of household devices [36]. Burak Gunay et al. proposed an electricity end-use disaggregation method based on regression models for commercial buildings [37]. Zaeri et al. applied multiple linear regression models to disaggregate the electricity and heating consumption in commercial buildings. These models were estimated by a least-square solver with a genetic algorithm [38].Sparse coding and DTW algorithm are widely used in NILM methods. Elafoudi et al. proposed a NILM method based on DTW algorithm to disaggregate the electricity consumption of residential building devices [39]. Kolter et al. used a sparse coding algorithm to train models for each device in a residential building with electricity consumption and applied these models to predict the electricity consumption of each device [40]. Matsui et al. applied a 0–1 sparse coding method to disaggregate the energy consumption of electric appliances from the total energy consumption of residential buildings [41]. Fourier analysis can be used to disaggregate different loads in the frequency domain. Dhar et al. took advantage of a Fourier series model to simulate real-time energy consumption of some campus buildings in Texas and obtained satisfactory results [42,43,44]. Ji et al. applied Fourier series models to disaggregate hourly electricity consumption of HVAC system terminal units from mixed lighting-plug sub-meters of commercial buildings [45]. The accuracy of the calculation results is high, although, in this study, the division of the date type is slightly rough, and a standardized and programmed calculation process is not achieved. Table 1 summarizes the details of above studies.Comprehensive information and detailed monitoring data of HVAC systems are the basis for building energy analysis and system control [46]. However, most previous studies have disaggregated the total electricity consumption of residential buildings into individual devices or the total electricity consumption of public buildings into system levels only. It is difficult for most existing commercial buildings to obtain the electricity consumption of HVAC terminal units directly. Therefore, an effective method is still needed to disaggregate the electricity consumption of HVAC terminal units from mixed sub-metering data.Previous studies on the characteristics of building electricity consumption have provided a theoretical basis for the disaggregation of mixed sub-metering data. The research conducted by Pandit et al. indicated that the electricity consumption of most commercial buildings follows an obviously periodical pattern [47]. The research by Braun et al. showed that sinusoidal functions could simulate the electricity demand in buildings [48]. They used a trigonometric formula to simulate the electricity consumption of commercial buildings. Afterward, the research by Claridge et al. indicated that the electricity consumption of lights and office equipment varied periodically and was not sensitive to outdoor weather conditions [49]. The investigations by Dhar et al. showed that for commercial buildings, electricity consumption had clear differences between weekdays and non-workdays [42,43,44]. Additionally, electricity consumption in different meteorological conditions should be treated differently. In our own previous research work, we used EnergyPlus to simulate the energy consumption of HVAC terminal units of a whole building. However, this method is time consuming because a detailed architectural model needs to be established and calibrated. When applied in another building, the model needs to be rebuilt [50].

It is a common practical problem that the electricity consumption of HVAC terminal units is mixed with lighting-plug sub-metering systems in most public buildings in China. This is one of the reasons why sub-metering data quality is poor in China. Many previous research studies focused on electricity consumption disaggregation for residential buildings, while only few research studies discussed the problem for public buildings. Some existing research studies adopted a Fourier series model in sub-metering data disaggregation. However, this algorithm is not suitable for categorical data. Different building types may require different date-type categorization methods. Therefore, in this study, we adopted a CART algorithm to disaggregate the real-time electricity consumption of the terminal units in a HVAC system, and we proposed a general calculation process to make it easier for other users, which would improve the data quality of sub-metering data in building energy management. The main novelties of this study are listed as follows:


This study adopts a CART algorithm to disaggregate the real-time electricity consumption of HVAC terminal units. It makes up for the deficiency that the Fourier series model is not suitable for categorical data.


A general disaggregation framework is proposed. It can be easily extended to different cases without constructing physical building energy models.

In this article, the methodology and model establishment processes are described in Section 2. In Section 3, two case studies in Shanghai are used to illustrate how to apply the proposed method. Discussions and limitations are presented in Section 4, and the conclusions are outlined in Section 5.



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