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Estimation of ambient temperature bin data from monthly average temperatures and solar clearness index. Validation of the methodology in two Greek cities

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  • Papakostas, K.
  • Bentoulis, A.
  • Bakas, V.
  • Kyriakis, N.

Abstract

Ambient temperature bin data are used for estimating the energy consumption in HVAC systems with air-source heat pumps and cooling equipment. In this paper a methodology for estimating the ambient temperature bin data, based on monthly average outdoor temperatures and solar clearness index, is presented. For the two most populated cities of Greece, namely Athens and Thessaloniki, the estimated data are compared to the bin data produced by statistical analysis of 10 years hourly dry-bulb temperature measurements. Both data sets were also used for estimating the heating and cooling energy requirements of a case study building. The results obtained are similar, with very small differences, suggesting that the proposed methodology can be used for estimating bin data for other cities.

Suggested Citation

  • Papakostas, K. & Bentoulis, A. & Bakas, V. & Kyriakis, N., 2007. "Estimation of ambient temperature bin data from monthly average temperatures and solar clearness index. Validation of the methodology in two Greek cities," Renewable Energy, Elsevier, vol. 32(6), pages 991-1005.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:6:p:991-1005
    DOI: 10.1016/j.renene.2006.04.002
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    References listed on IDEAS

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    1. Papakostas, K.T. & Sotiropoulos, B.A., 1997. "Bin weather data of Thessaloniki, Greece," Renewable Energy, Elsevier, vol. 11(1), pages 69-76.
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    Cited by:

    1. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    2. Konstantinos T. Papakostas & Dimitrios Kyrou & Kyrillos Kourous & Dimitra Founda & Georgios Martinopoulos, 2021. "Bin Weather Data for HVAC Systems Energy Calculations," Energies, MDPI, vol. 14(12), pages 1-23, June.
    3. Spandagos, Constantine & Ng, Tze Ling, 2018. "Fuzzy model of residential energy decision-making considering behavioral economic concepts," Applied Energy, Elsevier, vol. 213(C), pages 611-625.
    4. Papakostas, K. & Tsilingiridis, G. & Kyriakis, N., 2008. "Bin weather data for 38 Greek cities," Applied Energy, Elsevier, vol. 85(10), pages 1015-1025, October.

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    1. Papakostas, K. & Tsilingiridis, G. & Kyriakis, N., 2008. "Bin weather data for 38 Greek cities," Applied Energy, Elsevier, vol. 85(10), pages 1015-1025, October.
    2. Papakostas, K.T. & Michopoulos, A.K. & Kyriakis, N.A., 2009. "Equivalent full-load hours for estimating heating and cooling energy requirements in buildings: Greece case study," Applied Energy, Elsevier, vol. 86(5), pages 757-761, May.
    3. Papakostas, K.T, 1999. "Technical note Bin weather data of Athens, Greece," Renewable Energy, Elsevier, vol. 17(2), pages 265-275.

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