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Modeling energy intensity of residential space heating

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Abstract

The energy efficiency of heating systems has become a critical concern for academic study and public welfare worldwide. Developing a comparable measurement tool among various residential space heating systems can offer policy-makers a benchmark for monitoring efficiency, guide them in setting appropriate energy conservation goals and promote overall efficiency improvements through heating system transitions. In the present study, we establish a framework to accommodate heterogenetic space heating systems and model the energy efficiency for performance assessment. By taking the heating area and heating hours into account, two energy intensity indicators are developed. Data from the Chinese Residential Energy Consumption Survey (CRECS) is used to examine the energy intensity for Northern and Southern residences. Our results show opposite findings from other studies, and the interpretation is presented.

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Notes

  1. The divide between Northern and Southern China traces the Huai River and Qin Mountains near the latitude 33° north. More specifically, the North refers to the area which has winter for at least 90 days, with winter days defined as average daily temperatures less than or equal to 5 °C based on the calculation method of the Soviet Union. For most Northern areas, the heating period for district heating begins on November 15 and ends on March 15 of the following year. The heating period is sometimes longer, as the latitude degree increases.

  2. Strictly speaking, other factors, such as outdoor temperature and household size, should also be taken into account. However, this paper focused on the heating system gap. Some other studies also adopt similar strategies as the present paper (Chen et al. 2011; Building Energy Conservation Research Center of Tsinghua University 2013). These include (1) the annual heating energy consumption per heating area, presented as kgce/m2/a for coal dominated DHS and m3/m2/a for gas dominated DHS; (2) the heating energy consumption (denoted as E) per heating area (equal to floor area in DHS) in a certain period, for example, a space heating period or monthly average during heating season, measured as E/m2/space heating period, and E/m2/month; and (3) the monthly average heating energy consumption (E) per household, measured as E/month/household, calculated as E/m2/month times floor area per capita (m2/person, neglect of difference between floor area and heating area), and times persons per household (persons/household).

  3. Even in this scenario, the temperature and utility of IDHF may be lower than that of DHS.

  4. We compared the household characteristics from CRECS 2014 with official statistics and found that they are consistent, for example, the householder average age (37.9 vs. 37.7), dwelling area (116.4 vs. 121.2 m2), per capita food expenditure (4671.9 Yuan vs. 4494.0 Yuan), washing machine ownership per 100 households (82.2 vs. 83.7), and TV ownership per 100 households (114.4 vs. 119.2). Moreover, in CRECS 2014, the average heating energy consumption per square meter for DHS residents was 13.13 kgce/m2 (indoor). This number is close to the official record (primary supply intensity 18.5 kgce/m2 × (1 − pipe loss rate 30%) = 12.95 kgce/m2).

  5. Due to the omissions in the questionnaire design, we are hardly able to judge whether multiple devices are used to heat a single room. We make the following treatment: Set the heating area of the South-IDHs as the sum of reported heating area of each heating device. Therefore, potential bias may exist, overestimating the heating area of south-IDHS and resulting in an underestimation of the South-IDHS energy intensity.

  6. It should be noted that the acquisition and maintenance costs of heating infrastructure are not considered for simplification in this case.

  7. Besides energy input factors, other factors against applying the DHS in southern China include the high cost of reconstructing heating systems and buildings, the short heating season, etc.

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Acknowledgements

This study is supported by the National Natural Science Foundation of China (71622014, 41771564) and Ministry of Education of China (16YJA790049).

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Authors and Affiliations

  1. Academy of Macroeconomic Research, National Development and Reform Commission, Beijing, 100038, China

    Jin Guo

  2. Department of Energy Economics, School of Economics, Renmin University of China, No. 59, Zhongguancun Street, Beijing, 100872, China

    Shimei Wu, Jingqiu Hu & Chu Wei

  3. National Academy of Development and Strategy, Renmin University of China, Beijing, 100872, China

    Jin Guo & Chu Wei

  4. School of Business & Economics, University of Münster, 48143, Münster, Germany

    Chu Wei

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  1. Jin Guo
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Correspondence to Chu Wei.

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Guo, J., Wu, S., Hu, J. et al. Modeling energy intensity of residential space heating. Energy Efficiency 12, 921–931 (2019). https://doi.org/10.1007/s12053-018-9704-y

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