IDEAS home Printed from https://ideas.repec.org/a/vrs/otamic/v12y2020i1p2137-2146n8.html
   My bibliography  Save this article

Application of time series models for heating degree day forecasting

Author

Listed:
  • Kuru Merve
  • Calis Gulben

    (Ege University, Department of Civil Engineering, Bornova, Izmir, 35040, Turkey.)

Abstract

This study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD.

Suggested Citation

  • 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.
    • Handle: RePEc:vrs:otamic:v:12:y:2020:i:1:p:2137-2146:n:8
    DOI: 10.2478/otmcj-2020-0009
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/otmcj-2020-0009
    Download Restriction: no
    File URL: https://libkey.io/10.2478/otmcj-2020-0009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sarak, H & Satman, A, 2003. "The degree-day method to estimate the residential heating natural gas consumption in Turkey: a case study," Energy, Elsevier, vol. 28(9), pages 929-939.
    2. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
    3. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
    4. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    5. 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.
    6. Yu, Jinghua & Yang, Changzhi & Tian, Liwei & Liao, Dan, 2009. "A study on optimum insulation thicknesses of external walls in hot summer and cold winter zone of China," Applied Energy, Elsevier, vol. 86(11), pages 2520-2529, November.
    7. 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.
    8. Durmayaz, Ahmet & Kadıoǧlu, Mikdat & Şen, Zekai, 2000. "An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul," Energy, Elsevier, vol. 25(12), pages 1245-1256.
    9. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
    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. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    2. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    3. Forouzanfar, Mehdi & Doustmohammadi, Ali & Menhaj, M. Bagher & Hasanzadeh, Samira, 2010. "Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran," Applied Energy, Elsevier, vol. 87(1), pages 268-274, January.
    4. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    5. Dombaycı, Ö. Altan, 2009. "Degree-days maps of Turkey for various base temperatures," Energy, Elsevier, vol. 34(11), pages 1807-1812.
    6. Lanlan Li & Xinpei Song & Jingjing Li & Ke Li & Jianling Jiao, 2023. "The impacts of temperature on residential electricity consumption in Anhui, China: does the electricity price matter?," Climatic Change, Springer, vol. 176(3), pages 1-26, March.
    7. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
    8. Zhang, L.Y. & Jin, L.W. & Wang, Z.N. & Zhang, J.Y. & Liu, X. & Zhang, L.H., 2017. "Effects of wall configuration on building energy performance subject to different climatic zones of China," Applied Energy, Elsevier, vol. 185(P2), pages 1565-1573.
    9. Ayşe Özmen, 2023. "Sparse regression modeling for short- and long‐term natural gas demand prediction," Annals of Operations Research, Springer, vol. 322(2), pages 921-946, March.
    10. Valeria Annibaldi & Federica Cucchiella & Marianna Rotilio, 2020. "A Sustainable Solution for Energy Efficiency in Italian Climatic Contexts," Energies, MDPI, vol. 13(11), pages 1-16, June.
    11. Yang, Liu & Lam, Joseph C. & Tsang, C.L., 2008. "Energy performance of building envelopes in different climate zones in China," Applied Energy, Elsevier, vol. 85(9), pages 800-817, September.
    12. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    13. Liu, Long & Zhao, Jing & Liu, Xin & Wang, Zhaoxia, 2014. "Energy consumption comparison analysis of high energy efficiency office buildings in typical climate zones of China and U.S. based on correction model," Energy, Elsevier, vol. 65(C), pages 221-232.
    14. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    15. Kenisarin, Murat & Kenisarina, Kamola, 2007. "Energy saving potential in the residential sector of Uzbekistan," Energy, Elsevier, vol. 32(8), pages 1319-1325.
    16. Szoplik, Jolanta, 2016. "Improving the natural gas transporting based on the steady state simulation results," Energy, Elsevier, vol. 109(C), pages 105-116.
    17. Li, Xiaoma & Zhou, Yuyu & Yu, Sha & Jia, Gensuo & Li, Huidong & Li, Wenliang, 2019. "Urban heat island impacts on building energy consumption: A review of approaches and findings," Energy, Elsevier, vol. 174(C), pages 407-419.
    18. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    19. 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.
    20. Kaynakli, O., 2008. "A study on residential heating energy requirement and optimum insulation thickness," Renewable Energy, Elsevier, vol. 33(6), pages 1164-1172.

    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:vrs:otamic:v:12:y:2020:i:1:p:2137-2146:n:8. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.