Analysis of Principal Factors on Energy Consumption of Expressway Service Buildings

Abstract

As commercial transportation complexes, expressway service buildings have large passenger flow and a poor energy-saving effect, and have become the focus of energy conservation and emissions reduction efforts in the transportation field. At the same time, the particularity of the function determines that it is within the scope of no municipal supporting facilities, which renders them typical energy island-type buildings. This paper takes the expressway service buildings in a cold area as the research object, and carries out the correlation and partial correlation analysis of the factors influencing the operating energy consumption of the air-conditioning system. For the analysis of factors affecting the energy consumption of expressway service buildings during the operation period, considering that most of the service buildings are in the form of heating and cooling air conditioners, this paper chooses to represent the “refrigeration period” with a more obvious degree of influence. At the same time, during the operation period, because the ontological characteristics have been determined according to the analysis results, the outdoor meteorological characteristics are the main factors affecting the energy consumption of expressway service buildings. These include the dry bulb temperature and horizontal plane solar irradiance index, as well as the indoor comprehensive environment parameters: temperature, CO₂ concentration index, indoor personnel density index. Based on the above analysis, a low energy consumption operation strategy for the air-conditioning system is proposed. The results of this article are of great significance for the construction of energy consumption models for expressway service buildings and the adoption of low energy consumption strategies.

1. Introduction

As commercial transportation complexes, expressway service buildings have large passenger flow and a poor energy saving effect, and have become the focus of energy saving and emissions reduction efforts in the transportation field. At the same time, the particularity of the function determines that it is within the scope of no municipal supporting facilities, which renders them typical energy island-type buildings. The expressway service district has the energy supply characteristics of the “energy island type” and the energy consumption characteristic of a “high permeability coefficient.” Traditional expressway service buildings all need to build independent energy supply facilities, such as generator sets and heating boilers, and their investment remains high. Therefore, according to the energy consumption and energy consumption characteristics of existing expressway service buildings, it is of great significance to take energy-saving measures in the operation stage by analyzing the factors influencing air-conditioning operation energy consumption. Judging from the current on-site survey statistics of expressway service buildings in cold areas, the dual-purpose air-conditioning forms of “fan coil unit + fresh air system” are mostly used. At the same time, this paper analyzes the factors affecting the energy consumption of expressway service buildings during the operation period, mainly considering the “cooling period” with a more obvious impact.

Existing expressway service districts in cold regions were selected for statistical analysis of building energy consumption. The selected service districts include: 10 service districts belonging to the Daguang, Jingshi, Baojin, Zhangshi and Hengde expressways around the Xiongan New Area. Specifically, they include: Shenzhou, Xushui, Gaocheng, Dingzhou, Xiongxian, Cuizhuang, Hengshui, Quyang, Tangxian and Nanjiang expressway service districts. The energy consumption per unit area and total energy consumption data of the existing expressway service district are shown in Figure 1.

From Figure 1a, it can be concluded that the energy consumption per unit area of the four service districts of Shenzhou, Xushui, Gaocheng and Dingzhou is significantly lower than that of the other six service districts. Because the four service districts belong to the newly-built service districts on the Daguang and Beijing-Shijiazhuang Expressways, they use novel ground-source heat pump units and combine cooling and heating sources. The Xiongxian, Cuizhuang, Hengshui, Quyang, Tangxian and Nanjiang service districts are old service districts, and the cooling and heating use traditional methods, so the energy consumption data are quite different. It can also be concluded from the figure that the total energy consumption and the energy consumption per unit area between the expressway service districts in adjacent areas are also quite different, and the difference between the maximum value and the minimum value is large [1]. After actual measurement and analysis, it was found that the main reasons for the large differences in building energy consumption in expressway service districts are: different geographical locations and climatic conditions; different forms of cold and heat sources; large differences in passenger flow density and business conditions. Thus, the optimization measures are different. Among the 10 measured expressway service districts, the energy consumption density of the four newly built service districts is in the range of 150~200 kWh/(m²·a), and the energy consumption density of the six old service districts is 400~470 kWh/(m²·a). This greatly exceeds the energy consumption density of ordinary public buildings in the “China Building Energy Conservation Annual Development Research Report,” which shows that its energy saving potential is huge. Figure 1b shows the annual monthly energy consumption and the average temperature of the 10 service districts. It can be seen from Figure 1b that July to October is the main energy consumption time period in the expressway service district in cold regions. Due to the warm winter phenomenon in the central part of Hebei Province in the past two years, during the day, the average temperature during the working hours of the air conditioner can reach more than 5 °C, which leads to a reduction in heat loss and the energy consumption of the air conditioner during this period. At the same time, the window-to-wall ratio of buildings in the expressway service area is higher than that of general public buildings, and they are located in remote areas. Compared with urban buildings, the heat island effect is lacking, and the effect of solar radiation is more significant. Therefore, the energy consumption of air conditioning and refrigeration in summer is higher than that of heating in winter [2]. Compared with urban buildings, the heat island effect is lacking, and the effect of solar radiation is more significant. Therefore, the energy consumption of air conditioning and refrigeration in summer is higher than that of heating in winter. As the main building serving people in the expressway service district, the expressway service building has the highest energy consumption density in the expressway service district [3]. From the perspective of the energy consumption composition of expressway service buildings, the average energy consumption ratio of the 10 service buildings measured in 2019 is: air conditioning energy consumption, lighting energy consumption, coal or natural gas energy consumption, water heater energy consumption and other electrical equipment energy consumption The proportions of total energy consumption are: 42.6%, 15.1%, 16.8%, 7.2% and 18.3%, respectively. The energy consumption by the air-conditioning operation has become the primary factor in the energy consumption of expressway service buildings.

The three major factors that affect the energy consumption of expressway service buildings are the outdoor meteorological characteristics, ontological building characteristics and indoor environmental characteristics. Among them, the characteristics of the building body include overall characteristics such as building orientation, building scale, window-to-wall ratio, body shape coefficient and shading coefficient; thermal performance characteristics of the envelope structure such as the heat transfer coefficient, thermal inertia and ventilation times; and equipment characteristics such as the capacity of cold and heat sources, lighting and water heater power. For the operating energy consumption of the air-conditioning system, because the characteristics of the building itself have been determined, the influencing factors are mainly external factors, that is, the outdoor meteorological characteristics and indoor environmental characteristics. There are many factors that affect the energy consumption of air-conditioning system operation. According to the “General Specification for Building Energy Conservation and Renewable Energy Utilization” GB 55015-2021, the main influencing factors are external disturbance factors, that is, outdoor meteorological characteristics; and internal disturbance factors, that is, indoor environmental parameters. For highway service buildings, the outdoor meteorological elements that affect the energy consumption of air conditioners include five parameters: temperature, air pressure, humidity, wind direction and wind speed. According to the TMY(typical meteorological year) parameter composition of typical meteorological years determined in the “Meteorological Parameter Standard for Building Energy Conservation” (JGJ/T 346-2014), the factors affecting the energy consumption of air-conditioning operation include: dry bulb temperature, total solar radiation on the horizontal plane, outdoor air pressure, relative humidity, and outdoor wind speed, a total of five parameters. The indoor environmental factors that affect the energy consumption of air conditioners include four parameters: indoor temperature, humidity, CO₂ concentration and personnel density parameters. This paper specifically analyzes the factors influencing the operating energy consumption of the air-conditioning system, in order to obtain the main factors affecting the operating energy consumption of the air-conditioning system, and proposes low-energy consumption operation strategies.

2. State of the Art

The research and development of energy-saving adaptive technology in the building operation stage is mainly based on building usage characteristics and actual operation data. In 2006, Yang Jie [4] and others took Shanghai World Expo Exhibition Hall as the object of empirical analysis, simulated the building’s energy consumption and comprehensively analyzed the key influencing factors, and concluded that the main factor affecting energy consumption is passenger flow and the outer protective structure. In 2009, Sun Shengnan [5] and others took a large passenger station as the object of analysis. The building energy consumption simulation value was the dependent variable and the passenger flow was the independent variable. In 2012, Mambo [6] et al. analyzed the engineering response to the carbon emission problem of an existing terminal building, which is to control and integrate the indoor environmental system with external conditions and passenger flow. The unique properties, comfort standards, control point settings and control strategies of the indoor microclimate in airport terminals are discussed. In 2013, Deng Xiang [7] and others combined the theory of organizational behavior and took large shopping malls as the research object to study the relationship between customer behavior changes and building energy consumption, especially air conditioning energy consumption, and proposed to adjust the operation of air conditioners and other equipment according to customer behavior characteristics. In 2014, Vorovin [8] established a hybrid neural network model to analyze the influencing factors and simulate and verify the hourly power consumption data of the Finnish energy market, and proved the accuracy of the energy consumption prediction method. In 2015, Eshetu [9] et al. took a large waiting hall in Addis Ababa as an example, analyzed the impact of passenger flow on building energy consumption and the correlation between passenger flow and building energy consumption of large waiting halls. In 2016, Wang Tao [10] et al. took the expressway service area of Anhui province in a hot summer and cold winter area as an example, conducted a survey and questionnaire survey on the relevant factors influencing air-conditioning energy consumption, constructed a multiple linear regression model and proposed a passenger flow model. The energy consumption of air conditioners is one of the key factors affecting energy consumption in service districts. Based on the energy consumption monitoring of CABR near-zero energy consumption demonstration buildings of the China Academy of Building Research, Li Huai [11] and others proposed an effective method in the energy-saving operation stage, combining the characteristics of the energy system, aiming at comfort and system energy saving. The refined management strategy of behavioral energy saving, especially “optimized management of operation strategy based on actual operation data,” has a certain guiding significance for energy saving management in the operation stage of ultra-low energy consumption buildings in high-speed service districts. Chai [12] et al. analyzed and predicted the factors influencing road transportation energy consumption based on the BMA model. It is expected that China’s road transportation energy consumption will reach 347,363 ktoe by the end of 2020. Achour [13] used the logarithmic mean Divisia index method (LMDI) to summarize the factors influencing transportation energy consumption in Tunisia in terms of energy intensity, the transportation structure effect, transportation intensity effect, economic output and population size effect. Based on the operational characteristics of expressway toll stations and maintenance centers, Li [14] initially tried to use the Divisia logarithmic average index method to analyze the factors influencing energy to clarify the evolution of energy consumption. In 2017, Ren Nannan [15] and others proposed an ultra-low-energy building operation management strategy based on actual building operation data in severe cold regions. Relying on the actual operation data of demonstration buildings, data were collected and sorted in the cooling, heating and transition seasons, in order to provide a better understanding of how the building works. The ultra-low energy consumption buildings in the operation phase shall adjust the energy-saving measures based on the operation data. Bourdeau [16] analyzed the energy consumption of three campus buildings in Paris from 2014 to 2017, identified the main energy consumption factors, such as equipment and personnel, and decomposed and reconstructed the power demand curve. In 2018, Zhao Guanghui [17] and others proposed a green and intelligent building energy consumption integrated management system, which focused on the setting and control of the two parameters of passenger flow and building ambient temperature, and conducted a simulation study, which verified the validity of the control model. In 2020, Zheng Lintao [18] and others conducted an investigation and analysis of the passenger flow of five large shopping malls in Guangdong, built a model based on the actual passenger flow, and used the energy consumption simulation software system to simulate and analyze energy consumption, and compared it with the measured data. For comparison, the corresponding energy-saving measures are determined according to the model analysis results. These constructive methods adopted by scholars at home and abroad to study the factors influencing building energy consumption have strong reference value. Through the above literature and advanced research methods, we can draw the conclusion that the difference in energy consumption of buildings of different properties depends on a variety of factors. By analyzing the energy consumption levels and factors influencing different types of buildings and establishing energy consumption prediction models, we can effectively take energy-saving measures, reduce operation and maintenance costs, and accurately predict the energy consumption scale of similar buildings.

3. Methodology

The DesignBuilder software system takes Energyplus as the core and is widely used in building energy consumption simulation, and its accuracy has been verified [19]. In this paper, DesignBuilder was used to select Baoding in the cold area to establish a typical building model, and the indoor environment parameters were set to remain unchanged. At a certain time, the outdoor meteorological parameters remain unchanged. According to the energy consumption simulation, correlation and partial correlation methods are used to analyze the influence of various indoor environmental parameters on the energy consumption of air conditioners [20]. Therefore, the main factors affecting the energy consumption of expressway service buildings in cold regions are obtained.

3.1. DesignBuilder Energy Consumption Simulation

The CLIMATE-CONSULTANT software system is used to analyze the outdoor meteorological conditions in Baoding where typical buildings are located. CLIMATE-CONSULTANT thermal comfort and boundary parameters can be set individually, according to what is suitable for indoor comfort and the energy consumption analysis of public buildings [21]. CLIMATE-CONSULTANT can directly call the climate data of 8760 h per year in EPW format. The EPW format is the data format adopted by the building energy consumption simulation software EnergyPlus, which contains the climate data of tens of thousands of regions around the world. In this paper, the typical building area is assumed to be Baoding in a cold climate area, and the meteorological conditions used are the data of the Raoyang observation point. Its hourly meteorological data, including solar radiation, dry bulb temperature, dew point temperature, wind speed and other parameters that characterize climate characteristics in typical meteorological years, are shown in Figure 2; the outdoor dry bulb temperature is shown in Figure 3.

According to Figure 2 and Figure 3, it can be seen that in the Baoding area, April, May, September, and October are the transition seasons, and the dry bulb temperature is basically within the comfortable temperature range. When the temperature is lower or higher than the comfortable temperature, passengers can pass through, adding or removing clothing to achieve thermal comfort. From the hourly temperature point of view, the highest temperature in a day occurs at 11~21 o’clock. Based on Figure 3 and field investigation, the start and end time of the heating and cooling calculation period in Baoding can be determined, as shown in

Table 1. Starting and ending time of heating and cooling.

City	Heating Calculation Period		Cooling Calculation Period
Baoding	Start date (current year)	End date (next year)	Start date (current year)	End date (current year)
	15 November	15 March	1 June	1 September

.

Among them, the thermal parameters of the building and the enclosure structure, heating, ventilation and air conditioning parameters are set according to the “General Specification for Building Energy Conservation and Renewable Energy Utilization” GB 55015-2021, and the building type is selected as a Class B public building in a cold area. The typical building constructed in this paper has 2 floors above ground (without basement), the building area is 2350 m², the usable building area is 2200 m², the building base area is 46.8 m × 7.8 m × 3 m, the building height is 13.8 m, and the body shape coefficient is 0.25. The window–wall ratio is 0.15 for east and west, 0.35 for south and 0.21 for north.

3.1.1. Typical Building Model

The main functional areas of a typical building include the retail area, dining area and entertainment area on the first floor, and offices, conference rooms, and guest rooms on the second floor. Its facade and roof are appropriately simplified, as shown in Figure 4 and Figure 5.

3.1.2. Building Orientation

The Weather Tool is used to compare and analyze the annual solar radiation on the façades facing each other [22]. According to the solar radiation heat gain in the overheating period and the supercooling period in the whole year, the local relative optimal building orientation is obtained, and the EPW data in Beijing and Shijiazhuang are selected to determine the best orientation for the area. As shown in Figure 6, the orientation of typical highway service buildings adopts the best orientation of buildings in the area [23].

3.1.3. Cold and Heat Sources and Air Conditioning Systems

Due to the lack of a municipal energy supply system, geographical location and environmental advantages, the current highway service buildings often use the “ground source heat pump” cooling and heat source method [24]. The specific parameters of the ground source heat pump used in typical expressway service buildings are shown in

Table 2. Cold and heat sources of typical expressway service buildings.

Name	Parameter	Number
Heat pump unit WPS070.1B	Cooling capacity: 253.6 kW Power: 47.5 kW Heating capacity: 269.9 kW Power: 64.9 kW	1
Air-conditioning side circulating pump	46 m3/h Head: 32 mH2O Power: 11 kW	2 (1 for use and 1 for backup)
Ground source side circulating pump flow	51 m3/h Head: 28 mH2O Power: 11 kW	2 (1 for use and 1 for backup)

. The air-conditioning system adopts VRF multi-connected central air-conditioning system with IPLV ≥ 3.90.

3.1.4. Indoor Comfort Environment Range Setting

The Comfort Environment Index was referred to in setting interior design parameters. In this section, the parameter system takes a fixed limit, that is, the temperature is set to 26 °C, the relative humidity is 60%, and the fresh air volume is 30 m³/(h·person); the other parameters are set in the “General Specification for Building Energy Conservation and Renewable Energy Utilization” GB 55015-2021. The ventilation frequency of the room is set to be adjustable 0.2 times/hour~10 times/hour, and meets the “Design Code for Heating Ventilation and Air Conditioning of Civil Buildings” GB50736-2012.

3.1.5. Indoor Heat Disturbance

The personnel, equipment, lighting power and operating time of air conditioning and heating systems in the main functional rooms are based on the “General Specification for Building Energy Conservation and Renewable Energy Utilization” GB 55015-2021, as shown in

Table 3. Power density of personnel, lighting and equipment in main functional rooms.

Room Function	Dining AREA	Retail Area	Civic Area	Guest Room Area	Office
Light (W/m2)	10	10	9	7	9
Equipment (W/m2)	15	13	13	15	15
Personnel (m2/p)	8	8	8	20	10

and

Table 4. Operating hours of air-conditioning and heating systems.

Room Function	Dining Area	Retail Area	Civic Area	Guest Room Area	Office
Weekdays	Monday to Sunday, open from 6:00 to 21:00, closed at the rest of the time	Monday to Sunday, open from 0:00 to 24:00	Monday to Sunday, open from 0:00 to 24:00, but without air conditioning or heating system	Monday to Sunday, on from 0:00 to 24:00	From Monday to Sunday, on from 8:00 to 19:00, and off for the rest of the time
Holidays

.

3.1.6. Definition of Building Envelope

A typical expressway service building is a Class B public building, and the main parameters of the envelope structure are set according to the “General Specification for Building Energy Conservation and Renewable Energy Utilization” GB 55015-2021, as shown in

Table 5. Main envelope parameters.

Building		Parameter Settings
Enclosure	Design	Heat Transfer Coefficient U (W/(m2·K))	Solar Heat Gain Coefficient SHGC
	Exterior wall	0.39	/
	Roof	0.34	/
	Ground	1.39	/
	External window	2.50	0.61
	Exterior door	2.20	/

.

3.1.7. Simulation Results

The simulation time is selected as the non-heating season from April to October, and the simulation results of air-conditioning energy consumption are shown in

Table 6. Power density of personnel, lighting and equipment in main functional rooms.

Month	April	May	June	July	August	September	October
Air conditioning energy consumption (kWh/m2)	5.2	6.8	17.5	22.3	18.6	13.7	7.9

.

3.2. Analysis of Influence of Outdoor Meteorological Parameters on Energy Consumption of Air Conditioning Operation

The Pearson correlation coefficient in the SPSS (Statistical Product and Service Solutions) software system was used to characterize the relationship between the five parameters and the energy consumption of the air conditioner, and the parameters with strong energy consumption were selected. The indoor environmental parameters of typical expressway service buildings were set unchanged, and the monthly air-conditioning energy consumption values simulated by the DesignBuilder software system were analyzed by Pearson correlation and partial correlation with the monthly meteorological parameters, and the key factors affecting energy consumption were obtained.

3.2.1. Correlation Analysis

According to the “Meteorological Parameter Standard for Building Energy Conservation” (JGJ/T 346-2014), the composition of TMY parameters for a typical meteorological year was determined. The specific meteorological parameters selected for correlation analysis included five factors: dry bulb temperature, total solar radiation on the horizontal plane, outdoor air pressure, relative humidity and outdoor wind speed. The SPSS system was used to analyze the correlation of the selected factors influencing energy consumption. The monthly data from April to October were used, as shown in

Table 7. Building energy consumption and influencing factors in existing service districts.

Month	Air Conditioning Operation Energy Consumption (kWh/m2)	Average Dry Bulb Temperature (°C)	Total Solar Radiation on Horizontal Surface (MJ/m2)	Outdoor Air Pressure (mbar)	Relative Humidity (%)	Outdoor Wind Speed (m/s)
April	5.2	13.7	298.0	1014.6	59.7	2.5
May	6.8	20.5	461.2	1018.5	50.1	2.3
June	17.5	25.3	579.7	1002.8	78.1	1.9
July	22.3	27.2	613.4	1009.7	71.3	2.4
August	18.6	26.5	590.8	1005.9	76.8	1.7
September	13.7	21.3	521.3	1014.6	69.4	2.2
October	7.9	14.2	302.3	1010.3	70.2	1.6

. The Pearson correlation coefficient analysis was used [25]. The variables affecting the energy consumption of air-conditioning operation are all continuous data that obey the normal distribution and are independent of each other, and approximate the correlation between the distance variables. Therefore, the correlation of the variables can be directly measured by the Pearson correlation coefficient. The specific calculation formula of the Pearson coefficient method is shown in Equation (1). The closer the absolute value of the coefficient r is to 1, the stronger the correlation; the closer it is to 0, the weaker the correlation. If |r| = 1, it means that the two analytical objects are perfectly linearly related. If |r| = 0, it means that the two analytical objects are completely uncorrelated (not linearly related). The strength of the correlation between the two variables can be divided into the following grades: when |r| ≥ 0.8, it is regarded as a strong correlation; when 0.5 ≤ |r| < 0.8, it is regarded as a moderate correlation; when 0.3 ≤ |r| < 0.5, it is regarded as weak correlation; when |r| < 0.3, it indicates that the correlation between variables is very weak [26].

$$r=\frac{{\displaystyle \sum _{i=1}^n(x_i-\overline{x})(y_i-\overline{y})}}{\sqrt{{\displaystyle \sum _{i=1}^n{(x_i-\overline{x})}^2{\displaystyle \sum _{i=1}^n{(y_i-\overline{y})}^2}}}}$$

(1)

3.2.2. Partial Correlation Analysis

Partial correlation analysis means that when two variables are related to a third variable at the same time, the influence of the third variable is eliminated, and only the process of analyzing the degree of correlation between the other two variables is used to determine the R value of the correlation coefficient. The partial correlation coefficient r calculation and t test are shown in Formulas (2) and (3). Among the related variables, two factors, dry bulb temperature and total solar radiation on the horizontal plane, have influences on three factors: outdoor air pressure, relative humidity and outdoor wind speed [27]. Therefore, on the basis of correlation analysis, partial correlation analysis was further used to control the first two factors (second-order) and the last three factors (third-order) and to analyze the influence of other factors on the energy consumption of air-conditioning operation [28]. Partial correlation analysis was used, with t-test confidence intervals of 98% and 97%, and both-sided Sig values were less than 0.05.

$$r_i{}_{j(k)}=\frac{r_i{}_j-r_i{}_j{r}_{jk}}{\sqrt{1-r_i{{}_k}^2}\sqrt{1-r_j{{}_k}^2}}$$

(2)

$$t=\frac{r\sqrt{n-q-2}}{\sqrt{1-r^2}}$$

(3)

3.3. Analysis of the Influence of Indoor Environmental Parameters on the Energy Consumption of Air Conditioners

In order to calculate the influence of different parameters on the energy consumption of air-conditioning operation, according to the “Code for Design of Heating, Ventilation and Air-conditioning for Civil Buildings” GB50736-2012, different values of indoor environmental parameters are set within the comfortable range [29]. The energy consumption model of a typical expressway service building is still constructed according to 2.1, and the outdoor meteorological parameters are set to remain unchanged at a certain time, and the indoor temperature, humidity, CO₂ concentration and personnel density parameters are adjusted in turn, and the corresponding building energy consumption is calculated by using DesignBuilder software [30], in order to judge the influence of indoor environmental parameters on the energy consumption of air-conditioning operation [31]. The parameter indicators are set to 3–4 levels, that is, the indoor temperature settings in summer [25 °C, 26 °C, 27 °C, 28 °C], the humidity settings [30%, 50%, 70%] and the indoor temperature settings in winter [19 °C, 20 °C, 21 °C, 22 °C], while relative humidity is still set to [30%, 50%, 70%], CO₂ concentration is set to [400 ppm, 700 ppm, 1000 ppm] and personnel density is set to [10 m²/p, 20 m²/p, 30 m²/p].

3.3.1. Single Factor Analysis of the Influence of Air Conditioning Operation Energy Consumption

The outdoor meteorological parameters were set as fixed values at a certain time, and the summer working conditions were: dry bulb temperature 30.8 °C, horizontal plane solar radiation value 0.951 kW, outdoor air pressure 1005.3 mbar, relative humidity 67.5% and outdoor wind speed 2.1 m/s. The winter working conditions were: dry bulb temperature 6.8 °C, solar radiation value on horizontal plane 1.086 kW, outdoor air pressure 1020.6 mbar, relative humidity 36.7% and outdoor wind speed 1.9 m/s. When calculating the change in a parameter of the indoor environment, other parameters were set to fixed values according to 3.1.

3.3.2. Partial Correlation Analysis

The change in indoor population density had the most significant impact on the energy consumption of the air-conditioning system, and the indoor population density parameter also had a significant impact on the other three parameters [32,33]. Considering the coupling correlation of indoor environmental parameters, the SPSS system was still used to control the density of indoor personnel, and the partial correlation coefficient r_ij(k) was obtained to characterize the influence of various environmental parameters on the energy consumption of air-conditioning operation [34]. Using partial correlation analysis, the confidence interval of the t-test was 98%, and the two-sided value of Sig was less than 0.05.

4. Results

4.1. Analysis Results of Correlation between Outdoor Meteorological Factors and Air-Conditioning Energy Consumption

The correlation and partial correlation analysis results between outdoor meteorological factors and air-conditioning energy consumption are shown in

Table 8. SPSS-related analysis of building energy consumption in existing service districts.

Relevant Indicators		Air Conditioning Energy Consumption (kWh/m2)	Dry Bulb Temperature (°C)	Total Solar Radiation on the Horizontal Plane (MJ/m2d)	Outdoor Air Pressure (mbar)	Relative Humidity (%)	Outdoor Wind Speed (m/s)
Air conditioning energy consumption (kWh/m2)	Pearson Cor.	1.000	0.913 **	0.901 **	−0.674	0.740	−0.148
	Sig. (2-tailed)		0.004	0.006	0.097	0.057	0.752
Dry bulb temperature (°C)	Pearson Cor.	0.913 **	1.000	0.991 **	−0.524	0.506	−0.065
	Sig. (2-tailed)	0.004		0.000	0.227	0.247	0.890
Total solar radiation on the horizontal plane (MJ/m2d)	Pearson Cor.	0.901 **	0.991 **	1.000	−0.477	0.498	−0.027
	Sig. (2-tailed)	0.006	0.000		0.279	0.256	0.954
Outdoor air pressure (mbar)	Pearson Cor.	−0.674	−0.524	−0.477	1.000	−0.907 **	0.601
	Sig. (2-tailed)	0.097	0.227	0.279		0.005	0.153
Relative humidity (%)	Pearson Cor.	0.740	0.506	0.498	−0.907 **	1.000	−0.593
	Sig. (2-tailed)	0.057	0.247	0.256	0.005		0.161
Outdoor wind speed (m/s)	Pearson Cor.	−0.148	−0.065	−0.027	0.601	−0.593	1.000
	Sig. (2-tailed)	0.752	0.890	0.954	0.153	0.161

**. Correlation is significant at the 0.01 level (2-tailed).

and

Table 9. Partial correlation analysis results.

rij(k)	Third-Order Partial Correlation		Second-Order Partial Correlation
	Dry Bulb Temperature (°C)	Total Solar Radiation on the Horizontal Plane (MJ/m2d)	Outdoor Air Pressure (mbar)	Relative Humidity (%)	Outdoor Wind Speed (m/s)
Air Conditioning Energy Consumption (kWh/m2)	0.946	0.906	−0.582	0.789	−0.209

.

From the results of correlation and partial correlation analysis, the outdoor meteorological strong correlation parameters affected by the energy consumption of air-conditioning in expressway service buildings included two indicators: dry bulb temperature and total solar radiation on the horizontal plane.

4.2. Correlation Analysis Results between Indoor Environmental Factors and Air-Conditioning Energy Consumption

The relationship between the parameters of the indoor environment and the energy consumption of the air conditioner can be obtained from Figure 7.

The partial correlation analysis results of indoor environmental parameters and air-conditioning energy consumption are shown in

Table 10. Partial correlation analysis results.

rij(k)	Indoor Ambient Temperature	Relative Humidity	CO2 Concentration
Energy consumption of air conditioners in summer	−0.475	−0.063	0.517
Winter air conditioning energy consumption	0.374	−0.127	0.561

.

From Figure 7a and Table 10, it can be concluded that the correlation between indoor ambient temperature and the operating energy consumption of the air-conditioning system is strong in both winter and summer, with positive and negative correlations in winter and summer, respectively, and the correlation coefficient in winter is higher than that in summer; from Figure 7b and Table 10, it can be concluded that the indoor air humidity is negatively correlated with the operating energy consumption of the air-conditioning system in both winter and summer. The correlation in winter is greater than that in summer, and the correlations are both weak, with a 50% boundary. When the relative humidity is higher than the cut-off point, the correlation with the energy consumption of the air-conditioning operation is very weak and can be regarded as irrelevant; from Figure 7c and Table 10, it can be concluded that the indoor CO₂ concentration is related to the energy consumption of the air-conditioning system operation. There is a cut-off point of 800 ppm. When it is lower than this cut-off point, the CO₂ concentration is strongly correlated with the operating energy consumption of the air-conditioning system, and when it is higher than the cut-off point, there is a strong correlation; from Figure 7d and Table 10, it can be seen that the density of indoor personnel is strongly correlated with the operating energy consumption of the air-conditioning system.

5. Conclusions

From the results of correlation and partial correlation analysis, the outdoor-weather-related parameters that affect the energy consumption of air-conditioning in expressway service buildings include two indicators: dry bulb temperature and total solar radiation on the horizontal plane. The indoor environment strongly correlated parameters that affect the energy consumption of air-conditioning in expressway service buildings include indoor temperature and indoor population density. When the indoor CO₂ concentration index is lower than the cut-off point of 800 ppm, it is a strong correlation factor, and when it is higher than the cut-off point of 800 ppm, it is a weak correlation factor. At the same time, based on the above analysis, it can be concluded that the low energy consumption operation of the air conditioning system of the expressway service building should have the following characteristics:

(1)

The indoor ambient temperature has a significant impact on the operating energy consumption of the air-conditioning system. Combined with the temporary and streamlined characteristics of expressway service building personnel, the indoor temperature can be based on the obtained “acceptable threshold,” combined with “Civil Building Heating Ventilation and Air Conditioning Design Code” GB50736-2012, which is set according to the limit value;

(2)

The relative humidity above the 50% cut-off point has little effect on the operating energy consumption of the air-conditioning system, and the relative humidity below the 50% cut-off point has a significant impact on the operating energy consumption of the air-conditioning system. Therefore, in the daily operation process, it should be appropriately increased;

(3)

The CO₂ concentration has a significant impact on the operation of the air-conditioning system. The CO₂ concentration below the 800 ppm cut-off point has a gradually increasing influence on the energy consumption of the air-conditioning system. Therefore, in the daily operation process, the CO₂ concentration value (corresponding to the number of air changes) should not be calculated. If the setting is too low, it is necessary to comprehensively consider the setting of air quality requirements and energy saving requirements;

(4)

The density of indoor people has the greatest impact on the energy consumption of the air-conditioning system, and has a significant impact on the other three parameters of the indoor environment. Therefore, the precise setting of the “hourly room rate” index based on the change in indoor personnel density is of great significance for the energy-saving operation of air conditioners. The hourly occupancy rate of expressway service building personnel is significantly different from the value given in the relevant energy-saving codes and needs to be corrected.