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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

Front. Energy    2015, Vol. 9 Issue (2) : 217-230    https://doi.org/10.1007/s11708-015-0352-z
RESEARCH ARTICLE
Energy consumption of 270 schools in Tianjin, China
Jincheng XING, Junjie CHEN, Jihong LING()
School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
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Abstract

With the rapid development of education cause, the increasing energy consumption of school buildings is gradually causing widespread concern in recent years in China. This paper presented an analysis of energy consumption of 270 schools located in the city of Tianjin, China. The analysis focused specifically on calculating the space heating energy consumption indexes and non-heating energy consumption indexes of different types of schools, aiming at providing reliable and precise data for the government to elaborate policies and measures. The space heating energy consumption of schools adopting district heating and gas boiler were 92.04 kWh/(m2·a) and 64.25 kWh/(m2·a), respectively. Comparing to the schools without a canteen, the non-heating energy consumption index of schools with a canteen can increase by 8%–37%. Furthermore, clustering of different energy sources, the total primary energy consumption indexes were also presented. Space heating energy consumption accounted for approximately 64%–79% of the total primary energy consumption. When using time-sharing control and self-contained gas boiler instead of district heating, an amount of almost 27.8 kWh/(m2·a) and 77.5 kWh/(m2·a) can be saved respectively. Through extensive statistical analysis of the data collected, this paper demonstrated that gross floor area, heating energy source and canteen had a close relationship with the total primary energy consumption regarding complete schools. Eventually, a linear regression equation was established to make a simple prediction about the total energy consumption of existing complete schools and to estimate the energy consumption of complete schools to be built.

Keywords schools      energy consumption index      primary energy      energy saving      regression analysis     
Corresponding Author(s): Jihong LING   
Just Accepted Date: 28 January 2015   Online First Date: 16 March 2015    Issue Date: 29 May 2015
 Cite this article:   
Jincheng XING,Junjie CHEN,Jihong LING. Energy consumption of 270 schools in Tianjin, China[J]. Front. Energy, 2015, 9(2): 217-230.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0352-z
https://academic.hep.com.cn/fie/EN/Y2015/V9/I2/217
Fig.1  Efficiency of the gas boiler
Fig.2  Typical annual daily dry bulb temperature in Tianjin
Fig.3  Specific space heating energy consumption indexes per unit gross floor area of the 19 schools
Fig.4  Specific space heating energy consumption indexes per total number of students and teachers of the 19 schools
Classification Ha/[kWh·(m2·a)–1] Hp/[kWh·(p·a)–1]
School typology Complete school 57.02 698
Secondary school 62.85 440
Primary school 67.22 387
Kindergarten 109.24 553
Heating source District heating 92.04 692
Gas boiler 64.25 530
Tab.1  Mean values of specific heating energy consumptions
Fig.5  Non-heating energy consumption indexes per unit gross floor area of complete schools
Fig.6  Non-heating energy consumption indexes per unit gross floor area of secondary schools
Fig.7  Non-heating energy consumption indexes per unit gross floor area of primary schools
Fig.8  Non-heating energy consumption indexes per unit gross floor area of kindergartens
Fig.9  Non-heating energy consumption indexes per total number of students and teachers of complete schools
Fig.10  Non-heating energy consumption indexes per total number of students and teachers of secondary schools
Fig.11  Non-heating energy consumption indexes per total number of students and teachers of primary schools
Fig.12  Non-heating energy consumption indexes per total number of students and teachers of kindergartens
Energy form Average net calorific value Conversion factor
Electricity 3600?kJ/kWh 2.84?kWh/kWha
Natural gas 35375?kJ/Nm3 9.83?kWh/Nm3
Raw coal 20908?kJ/kg 5.814?kWh/kg
District heating 0.454?kWh/MJb
Tab.2  Conversion factors of consumed energy
Energy source Minimum Mean Maximum N
kWh/(m2·a) kWh/(p·a) kWh/(m2·a) kWh/(p·a) kWh/(m2·a) kWh/(p·a)
District heating 106.1 881.8 150.4 1131.5 176.1 1478.9 3
Natural gas 21.8 228.8 72.9 598.3 131.3 1591.7 16
Raw coal 51.5 467.3 154.4 1369.3 344.6 4123.2 86
Tab.3  Descriptive statistics of primary heating energy consumption
School typology PHECa PNHECb TPECc
kWh/(m2·a) kWh/(p·a) kWh/(m2·a) kWh/(p·a) kWh/(m2·a) kWh/(p·a)
Complete school 122.31 1186.1 65.92 1022.7 188.23 2208.8
Secondary school 132.99 1018.8 40.09 407.1 173.08 1425.9
Primary school 140.88 1063.7 35.86 242.7 176.74 1306.4
Kindergarten 163.00 937.7 80.07 508.7 243.07 1446.4
Tab.4  Primary energy consumption indexes
Variable Value Instruction
Gross floor area (X1) [xxxx] [m2]
Total number of students and teachers (X2) [xxxx] [p]
Construction year(X3) 0 Construction year before 2005 containing 2005
Heating energy source (X4) 1 Construction year after 2005
0 Natural gas
1 District heating
2 Raw coal
Rank of school(X5) 0 Non-key school
1 Key school
Canteen(X6) 0 No canteen
1 Additional canteen
Tab.5  Independent variables for selected complete schools
Schools TPEC/(GWh·a–1) X1 X2 X3 X4 X5 X6
A1 0.767 7948 533 0 1 0 0
A2 1.543 6890 1448 1 1 1 1
A3 3.109 10322 1595 0 2 0 0
A4 1.274 19114 1419 0 0 0 0
A5 1.675 19758 1826 0 0 1 0
A6 4.959 20774 1597 0 1 0 1
A7 11.469 41000 2139 1 1 0 1
A8 5.562 27441 1606 0 1 1 1
A9 6.607 16873 2254 0 2 0 1
A10 6.721 40480 2718 1 0 0 1
A11 1.777 7741 1465 0 1 0 1
A12 10.576 45406 1242 1 1 1 1
A13 16.147 67637 2606 0 1 0 1
A14 8.334 33725 1311 0 1 1 1
A15 5.277 32254 1971 1 0 1 1
A16 15.918 63843 3097 0 1 1 1
A17 3.711 10984 2001 0 2 1 0
A18 3.150 25330 3373 0 0 1 0
A19 5.226 20938 2746 0 1 1 1
A20 4.538 13247 1694 0 2 1 1
Tab.6  Statistics of basic data
Independent variables Pearson correlation coefficient Unrelated significant level Number of samples
X1 0.987 0.000 20
X2 0.239 0.411 20
X3 0.117 0.691 20
X4 0.929 0.000 20
X5 –0.149 0.610 20
X6 0.525 0.045 20
Tab.7  Summary of partial correlation value
Model Unstandardized coefficients t Sig. Collinearity statistics
bn Std. Error Tolerance VIF
Constant –3789026 830138 –1.429 0.074
X1 273 23 9.410 0.000 0.862 1.160
X4 2415464 505443 1.330 0.000 0.867 1.153
X6 752621 357662 2.289 0.037 0.782 1.278
Tab.8  Coefficients of regression model
Fig.13  Comparison of actual and predicted values of TPEC for complete schools
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