1. Introduction
Of China’s total energy emissions, the proportion of building-related carbon emissions is 51.2%. In particular, the proportion of carbon emissions from the operational phase of buildings is 21.9% [
1]. Heating is one of the major causes of energy consumption in the operational phase of buildings, especially in northern China. It is important to reduce heating energy consumption and improve heating energy efficiency under indoor temperatures to meet the needs of the normal lives of residents [
2,
3,
4]. Smart heating has greater advantages than traditional heating systems in terms of energy savings, regulation, and fault diagnosis, and has huge development potential and marketing prospects [
5]. Nowadays, temperature, pressure, and flow sensors are usually installed at every important monitoring point in a heating system. Through a monitoring and data-acquisition system, operational data can be monitored and recorded in real time to support the operational maintenance of a heating system [
6,
7]. The heating system accumulates a large amount of time-series data, which represent the state changes of the pipe network during the operation of the heating system. In the context of the big-data era, identifying the step data caused by disturbances and analyzing their processes and characteristics is the focus of temporal analysis of data.
Data collected by a heating system over a certain period of time may suddenly fluctuate from a stable state for some reason, then return to the stable state. Data with a step phenomenon between the two stable states are called step data. Step data indicate that the hydraulic status of a pipe network changes over time; therefore, identifying step data involves determining the time point and the numerical change of the hydraulic state of the pipe network. The reasons for the change in fluid states can be divided into two categories—normal pipe network regulation and faults in the pipe network system.
There are three main physical quantities that operate in a heating system: temperature, pressure, and flow rate. The precise regulation of temperature is easier to achieve than the regulation of pressure or flow. The main reason for temperature regulation is a change in building thermal load; therefore, many people construct prediction models based on a building’s energy load or other related time-series data to achieve the goal of improving the accuracy of load prediction. A complete set of temperature regulation logic is then established through high accuracy load prediction [
8,
9]. Because a heating system has a complex pipe network structure, it is very difficult to accurately analyze the hydraulic state of the pipe network operation; the physical quantities that indicate the hydraulic state of the pipe network are flow and pressure.
A regulation system is an important part of smart heating [
10]. The regulation system senses abnormal pressure data and flow data and immediately takes appropriate adjustment measures, such as adjusting valves, to bring the flow and pressure back to normal. However, since each regulation takes a different time to restore the state of the network, and the system cannot distinguish whether the cause of abnormal data is normal operation or network failure, the system will frequently issue regulation commands that result in oscillations in the network. The main cause of this problem occurs during the analysis stage of a smart heating control system, which can sense abnormal data but cannot analyze the physical relationships of step data, so the system cannot diagnose the cause of the step data, and then issues wrong control orders.
The failure of a heating system refers to the partial or total loss of the system’s ability to transport the heating medium. The occurrence of failure can reduce the operating efficiency of the system or even cause efficiency to disappear completely [
11]. Many people build corresponding detection models based on wavelet analysis and the threshold method; such models can identify fault data in a set of data [
12,
13,
14]. HVAC systems have complex structures, so the direct analysis of an HVAC system’s raw data is difficult. The processing of raw data by wavelet analysis can obtain characteristic data of an HVAC system, which provide the main operational information about the system and diagnose system faults [
15]. Data in many heating systems is monitored and uploaded through automated equipment. The threshold method can identify fault data in such automatically uploaded data and classify the fault data into three groups: an improper heat load pattern, a low annual average temperature difference, and poor equipment control. Faults from the first two fault groups are relatively easy to solve, but faults from the third group are difficult to solve [
16]. However, as universal mathematic methods, both wavelet analysis and the threshold method are applied to real measurement data—they cannot describe the physical relationships between variables in HVAC systems well.
Wavelet analysis has a high accuracy rate when the difference between faulty data and normal data is large. However, small fault data differs very little from the normal data. Because the physical relationships between variables in an HVAC system cannot be clearly described, this leads to a low success rate for wavelet analysis in the diagnosis of small faults. Although the threshold method can classify the fault data of a heating system, it has difficulty solving faults from the equipment fault group, because there are many devices in the system and each device may cause a fault. In addition, the threshold method does not accurately describe the physical relationships between variables in the system, so the cause and location of a failure cannot be accurately analyzed; thus, the device that generated the failure cannot be analyzed.
The current data processing of the existing operating conditions of a heating network is mainly applied in two ways. One way is the identification of abnormal data caused by the failure of the network; the other way is the prediction of the building heat load. The mechanisms of both the identification algorithm and the prediction algorithm can be attributed to the processing of discrete data via statistics. Such processing ignores the characteristics of heating network operation data over time, and its results ignore the inherent time-series coupling relationship between the flow and pressure of each pipeline of supply water and return water. The actual pipe network operation processes consist of a non-constant flow, and the flow and pressure data fluctuate with time. Because the fluctuation process of pipe network data must be time-dependent, the characteristics of data changes with time are lost when the discrete data processing method is applied to actual heating pipe network data. Therefore, it is clear that the realization of smart heating must solve the problem that arises because the physical relationship between variables in a heating system cannot be precisely described [
17,
18].
The aim of the research in this paper is to calculate the time-series fluctuation of multiple variable data sets, based on data correlation. The time-series fluctuation calculation identifies the change characteristics of multiple variable data sets over time. Therefore, this paper can describe the physical relationships of multiple variables in a pipe network with time, based on the time-series change characteristics.
The measured data of a heating system can be continuously affected by many factors. For example, the factors affecting the supply water flow data of a primary network include heat sources, pipes, valves, pumps, and other pipe network equipment. The result of a combination of factors is that the measured data do not match the physical relationships between the variables in a system. When an anomaly is detected in pipe network data, it indicates that the effect of a certain factor on the data is suddenly strengthened. This factor dominates the changes in the data, such as the start of the relay pumping station, leading to an increase in the water flow and pressure of the pipe network. At this point, it is necessary only to analyze the influence of this factor on the data to understand the actual operating status of the pipe network. In the pipe network’s actual measurement data, a sudden change of data from the time-series perspective is mainly manifested in two ways: an increase or a decrease of data values and an acceleration of the data change rate [
19,
20].
This paper proposes the application of the time-series fluctuation research method to identify abnormal data with sudden changes. Based on the numerical value of the data and the rate of change of the data—that is, the average value of the data and the degree of data fluctuation—this paper proposes the concept of time-series disturbance, through which the physical relationships among the variables of a heating system and the transmission process of the disturbance causing data anomalies in a pipe network are demonstrated. In addition, two problems are solved by time-series disturbance: first, the physical relationships among the variables of a heating system are precisely described; second, the transmission process of the disturbance that causes a data anomaly in the pipe network is discovered.
The identification of step data is important for fault diagnosis and for the precise regulation of a pipeline network. Because flow and pressure are the two most important physical quantities for describing the hydraulic regime of a pipe network, the time-series data that this paper focuses on are flow and pressure data. This paper aims to discover the value of step data and proposes a new method to identify abnormal data. The main contents of the proposed method are summarized as follows:
- (1)
Time-series fluctuation is proposed to represent the evolution of time-series data over time.
- (2)
A method for the cyclic identification of step data is proposed, because different sets of data have different data characteristics.
- (3)
The time interval of step data is classified to judge the data relationships among different data sets.
- (4)
The concept of time-series disturbance is proposed to quantify the degree of data anomalies and identify the transmission processes of significant disturbance in a pipeline network.
The rest of this paper is organized as follows. For ease of understanding,
Section 2 introduces the time-series fluctuation research method in detail.
Section 3 analyzes the results of this research and its application in smart heating.
Section 4 provides the main conclusions of this study.
3. Analysis of Results and Discussion
We calculated the time-series fluctuation value for 6 sets of data, with 8640 pieces in each data set, and identified step data points with time intervals. Then, we selected two typical time intervals to calculate the time-series disturbance of flow and pressure, respectively.
Figure 3 shows the calculated time-series fluctuation value for the six sets of data, indicating the basic relationship between the time-series fluctuation values and the fluctuation patterns of the raw data with time.
Table 6 illustrates that the categories of step data are the same for different data sets. In
Figure 6, the data monitoring points are different for different data sets; however, the variation pattern of the time-series fluctuation values of the step data is similar, indicating that the disturbance causing the data anomaly completes the spatio–temporal transmission through the fluid in the tube.
The time-series disturbance ratio of the flow data in
Figure 8 is 0.855~1.200, and the average time-series disturbance ratio is 0.991, which indicates that the time-series disturbance of the two sets of data is highly fitted. Similarly, the time-series disturbance ratio of the pressure data in
Figure 10 is 0.857~1.180 and the average time-series disturbance ratio is 0.994. We concluded that the time-series disturbance to quantify the degree of data anomalies is reasonable, and the high degree of curve fit indicates that the significant perturbation causing data anomalies in different data sets is the same. It is important to note that the significant disturbances demonstrated in this study were transmitted in different data sets, which were the same physical quantity data.
In the long pipe model, the flow rates at both ends of the pipe are equal; however, in the actual pipe network flow data, it is difficult to equalize the supply flow rate and the return flow rate. In addition to the presence of leakage water, this is because the measurement of flow data is affected by many other devices. In the time interval of the step data, this paper demonstrates that the time-series disturbance of supply water flow and return water flow are equal and describes the basic physical relationships among the heating system variables. In the long pipe model, the continuous flow of liquid in the pipe is due to the unequal pressure at the two ends of the pipe, and the liquid flows from the high-pressure end to the low-pressure end. In the actual pipe network pressure data, the supply water pressure is greater than the return water pressure. The pipe network has a constant pressure at the return end, resulting in the return water pressure being almost constant during the time interval of the abnormal data, so the evolution of the pressure difference over time is similar to the supply water pressure. The pressure difference is less than the supply water pressure; however, this paper demonstrates that the time-series disturbance of the supply water pressure and the pressure difference are equal during the time interval of the step data. The pressure difference is what makes the liquid flow in the pipe network, so the disturbance that causes the abnormal pressure data originates from the supply water side. The above analysis describes a new physical relationship among heating system variables. The results show that the time-series fluctuation research proposed in this paper is a reliable method for analyzing fluid data in pipe networks.
Smart heating has five characteristics: self-perception, self-analysis, self-diagnosis, self-decision, and self-learning [
24].
Figure 11 interprets smart heating from the perspective of step data.
The prerequisite for the realization of smart heating is the reasonable application of a large amount of measured data, and the focus is on how to accomplish the goal of a “data-driven model”. In this paper,
Section 2.1 and
Section 2.2 proposed a new method of identifying step data, which was applied to the self-perception characteristic;
Section 2.3 and
Section 2.4 proposed the time-series disturbance to analyze the evolution of data over time, which was applied to the self-analysis characteristic. The research carried out in this paper provides a preparatory study for the self-diagnosis of smart heating.
An important contribution of this paper is to extract and retain the time characteristics of the data set from the perspective of time sequence, so as to provide technical support for the future intelligent pipeline network. The way to realize smart heating is to effectively apply time-series data to the heating field. The extraction of the characteristics of time-series step data from the heating network data set is the great significance of this research. Smart heating must be realized through AI or machine learning. The research carried out in this paper provides better technical support for the intelligent application of machine learning in heating. Our next direction is to better realize intelligent heating based on the combination of time-series fluctuation theory and an intelligent algorithm.
4. Conclusions
A prerequisite for realizing smart heating is the reasonable use and analysis of a large amount of data from the heating network, including historical operation data and real-time update data. Currently, many studies on data do not adequately describe the physical relationships among the variables in HVAC systems. Based on the relevant research background, this paper proposed a time-series fluctuation research method that can be applied to the measured data of a hot water heating network, which can identify abnormal data and quantify the degree of data abnormality.
The time-series fluctuation calculation of six sets of data in the study identified step data points and their corresponding time intervals, and the time intervals were divided into long and short intervals. According to the difference percentages and values of the time-series fluctuation between the data at both ends of the intervals, the long intervals were divided into removable intervals and jump intervals. In our research, it was confirmed from the perspective of time duration and heating network operation data information that the anomalies of different data are caused by the same factors based on the flow data categories and the variation patterns between time-series fluctuations. The concepts of time-series disturbance of flow and pressure were proposed, based on the principle of long pipe impedance calculation in hydraulics. The results show that it is reasonable to quantify the data anomalies by temporal perturbations. Based on the analysis of the case study, the time-series disturbance ratio of flow was 0.855~1.200 and the mean value was 0.991, while for the time-series disturbance ratio of pressure, the value was 0.857~1.180 and the mean value was 0.994, indicating that strong disturbances were transmitted from the supply line to the return line. The time-series fluctuation research method applied in this paper provides a feasible and convenient new research idea for self-perception and self-analysis in smart heating.