IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1008-d1325682.html
   My bibliography  Save this article

Using the Degree-Day Method to Analyze Central Heating Energy Consumption in Cities of Northern China

Author

Listed:
  • Yangyi Song

    (Center of Architecture Research and Design, University of Chinese Academy of Sciences, Beijing 100190, China)

  • Ao Du

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

  • Tong Cui

    (Center of Architecture Research and Design, University of Chinese Academy of Sciences, Beijing 100190, China)

Abstract

In the context of global population growth and energy scarcity, building energy consumption has become a critical issue with implications for the sustainable development of human society. Winter heating consumption constitutes a large portion of total energy used in buildings, especially in regions with cold climates. This paper employs the degree-day method to analyze the energy consumption of central heating in northern Chinese cities. The study sample consists of 60 target cities, including 30 located in severe cold regions and the remaining 30 in cold regions. By utilizing heating energy consumption and climate data from 2019, the relationships between heating intensity (kWh/m 2 ) and heating degree days (HDDs) are established for the selected cities. Additionally, statistical analysis and model comparisons are conducted. The results show strong positive correlations between heating intensity and HDDs in both severe cold regions and cold regions, with the actual heating base temperatures for the two regions being 21 °C and 22.3 °C, respectively. Moreover, the deviation index of heating intensity is introduced to analyze the energy consumption characteristics of central heating in northern cities from three perspectives: city size, level of heating development, and geographical regions. The analysis suggests that cities with large population, strong economies, and high levels of development exhibit better energy-saving performance. Lastly, several improvement suggestions are proposed to address the potential problems related to energy conservation of central heating systems in cities of northern China.

Suggested Citation

  • Yangyi Song & Ao Du & Tong Cui, 2024. "Using the Degree-Day Method to Analyze Central Heating Energy Consumption in Cities of Northern China," Sustainability, MDPI, vol. 16(3), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1008-:d:1325682
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1008/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/3/1008/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Büyükalaca, Orhan & Bulut, Hüsamettin & YIlmaz, Tuncay, 2001. "Analysis of variable-base heating and cooling degree-days for Turkey," Applied Energy, Elsevier, vol. 69(4), pages 269-283, August.
    2. Yu, Yanzhe & Cheng, Jie & You, Shijun & Ye, Tianzhen & Zhang, Huan & Fan, Man & Wei, Shen & Liu, Shan, 2019. "Effect of implementing building energy efficiency labeling in China: A case study in Shanghai," Energy Policy, Elsevier, vol. 133(C).
    3. De Rosa, Mattia & Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Heating and cooling building energy demand evaluation; a simplified model and a modified degree days approach," Applied Energy, Elsevier, vol. 128(C), pages 217-229.
    4. Kohler, M. & Blond, N. & Clappier, A., 2016. "A city scale degree-day method to assess building space heating energy demands in Strasbourg Eurometropolis (France)," Applied Energy, Elsevier, vol. 184(C), pages 40-54.
    5. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. D'Amico, A. & Ciulla, G. & Panno, D. & Ferrari, S., 2019. "Building energy demand assessment through heating degree days: The importance of a climatic dataset," Applied Energy, Elsevier, vol. 242(C), pages 1285-1306.
    2. Papada, Lefkothea & Kaliampakos, Dimitris, 2016. "Developing the energy profile of mountainous areas," Energy, Elsevier, vol. 107(C), pages 205-214.
    3. Rosa Francesca De Masi & Gerardo Maria Mauro & Silvia Ruggiero & Francesca Villano, 2023. "Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves," Energies, MDPI, vol. 16(16), pages 1-23, August.
    4. Jiang, Dachuan & Xiao, Weihua & Wang, Jianhua & Wang, Hao & Zhao, Yong & Li, Baoqi & Zhou, Pu, 2018. "Evaluation of the effects of one cold wave on heating energy consumption in different regions of northern China," Energy, Elsevier, vol. 142(C), pages 331-338.
    5. Kuru Merve & Calis Gulben, 2020. "Application of time series models for heating degree day forecasting," Organization, Technology and Management in Construction, Sciendo, vol. 12(1), pages 2137-2146, January.
    6. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    7. Küçüktopcu, Erdem & Cemek, Bilal, 2018. "A study on environmental impact of insulation thickness of poultry building walls," Energy, Elsevier, vol. 150(C), pages 583-590.
    8. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    9. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
    10. María Herrando & Antonio Gómez & Norberto Fueyo, 2022. "Supporting Local Authorities to Plan Energy Efficiency in Public Buildings: From Local Needs to Regional Planning," Energies, MDPI, vol. 15(3), pages 1-17, January.
    11. Ciulla, G. & D'Amico, A. & Lo Brano, V. & Traverso, M., 2019. "Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level," Energy, Elsevier, vol. 176(C), pages 380-391.
    12. Omar, M.N. & Samak, A.A. & Keshek, M.H. & Elsisi, S.F., 2020. "Simulation and validation model for using the energy produced from broiler litter waste in their house and its requirement of energy," Renewable Energy, Elsevier, vol. 159(C), pages 920-928.
    13. Sukjoon Oh & John F. Gardner, 2022. "Large Scale Energy Signature Analysis: Tools for Utility Managers and Planners," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    14. Domenico Palladino & Flavio Scrucca & Nicolandrea Calabrese & Grazia Barberio & Carlo Ingrao, 2021. "Durum-Wheat Straw Bales for Thermal Insulation of Buildings: Findings from a Comparative Energy Analysis of a Set of Wall-Composition Samples on the Building Scale," Energies, MDPI, vol. 14(17), pages 1-19, September.
    15. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    16. Chai, Jiale & Huang, Pei & Sun, Yongjun, 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions," Energy, Elsevier, vol. 185(C), pages 176-189.
    17. Ucar, Aynur, 2010. "Thermoeconomic analysis method for optimization of insulation thickness for the four different climatic regions of Turkey," Energy, Elsevier, vol. 35(4), pages 1854-1864.
    18. Bulut, Hüsamettin & Büyükalaca, Orhan & YIlmaz, Tuncay, 2001. "Bin weather data for Turkey," Applied Energy, Elsevier, vol. 70(2), pages 135-155, October.
    19. Lin, Haiyang & Wang, Qinxing & Wang, Yu & Liu, Yiling & Sun, Qie & Wennersten, Ronald, 2017. "The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model," Applied Energy, Elsevier, vol. 202(C), pages 248-258.
    20. Perera, D.W.U. & Winkler, D. & Skeie, N.-O., 2016. "Multi-floor building heating models in MATLAB and Modelica environments," Applied Energy, Elsevier, vol. 171(C), pages 46-57.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1008-:d:1325682. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.