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A new approach for predicting cooling degree-hours and energy requirements in buildings

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  • Oktay, Z.
  • Coskun, C.
  • Dincer, I.

Abstract

This study develops a novel approach to predict the outdoor temperature fluctuations during daytime as a dimensionless temperature variation coefficient. In this approach, the daily outdoor temperature trend is established by using the daily maximum and minimum temperatures. A case study is performed to calculate the cooling degree-hours for 58 cities in different geographical regions of Turkey as a case study for the present approach. The results are then compared with the published data. The other advantage of this approach is that it allows the prediction of monthly cooling degree-hours for buildings.

Suggested Citation

  • Oktay, Z. & Coskun, C. & Dincer, I., 2011. "A new approach for predicting cooling degree-hours and energy requirements in buildings," Energy, Elsevier, vol. 36(8), pages 4855-4863.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:8:p:4855-4863
    DOI: 10.1016/j.energy.2011.05.022
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    References listed on IDEAS

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    Cited by:

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    6. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
    7. Khuram Pervez Amber & Muhammad Waqar Aslam & Faraz Ikram & Anila Kousar & Hafiz Muhammad Ali & Naveed Akram & Kamran Afzal & Haroon Mushtaq, 2018. "Heating and Cooling Degree-Days Maps of Pakistan," Energies, MDPI, vol. 11(1), pages 1-12, January.
    8. Zongming Yang & Mykola Radchenko & Andrii Radchenko & Dariusz Mikielewicz & Roman Radchenko, 2022. "Gas Turbine Intake Air Hybrid Cooling Systems and a New Approach to Their Rational Designing," Energies, MDPI, vol. 15(4), pages 1-18, February.
    9. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.
    10. Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.

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