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Simulating the thermal behaviour of a building during summer period in the urban environment

Author

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  • Assimakopoulos, M.N.
  • Mihalakakou, G.
  • Flocas, H.A.

Abstract

In this study an attempt is made to investigate the impact of the urban heat island (UHI) effect on the energy consumption of a building using both, an accurate, extensively validated, transient simulation model and a neural network one. The energy consumption is calculated for a typical building during the summer period at 20 different sites of the Greater Athens area with the aid of the TRNSYS model. It was found that the UHI affects significantly the thermal behaviour of the building, implying much lower cooling load cost and energy cost in the suburban areas. A neural network model was then appropriately designed and tested for the estimation of the energy consumption, employing as an input, the UHI intensity. The results of both methods were tested and compared and it was found that there is a good agreement between the cooling rate values calculated by the TRNSYS programme and those derived by the neural model. Moreover, the employment of the neural model helped in quantifying the contribution of each input parameter in the calculation of the cooling rate, and it was demonstrated that the UHI effect is a predominant parameter, affecting considerably the energy consumption of a building in the Mediterranean region.

Suggested Citation

  • Assimakopoulos, M.N. & Mihalakakou, G. & Flocas, H.A., 2007. "Simulating the thermal behaviour of a building during summer period in the urban environment," Renewable Energy, Elsevier, vol. 32(11), pages 1805-1816.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:11:p:1805-1816
    DOI: 10.1016/j.renene.2006.08.003
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    Citations

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    Cited by:

    1. Bagiorgas, H.S. & Mihalakakou, G., 2008. "Experimental and theoretical investigation of a nocturnal radiator for space cooling," Renewable Energy, Elsevier, vol. 33(6), pages 1220-1227.
    2. Deng, Ji-Yu & Wong, Nyuk Hien & Zheng, Xin, 2021. "Effects of street geometries on building cooling demand in Nanjing, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    3. Hirano, Y. & Fujita, T., 2012. "Evaluation of the impact of the urban heat island on residential and commercial energy consumption in Tokyo," Energy, Elsevier, vol. 37(1), pages 371-383.
    4. Cui, Ying & Yan, Da & Hong, Tianzhen & Ma, Jingjin, 2017. "Temporal and spatial characteristics of the urban heat island in Beijing and the impact on building design and energy performance," Energy, Elsevier, vol. 130(C), pages 286-297.
    5. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    6. Frayssinet, Loïc & Merlier, Lucie & Kuznik, Frédéric & Hubert, Jean-Luc & Milliez, Maya & Roux, Jean-Jacques, 2018. "Modeling the heating and cooling energy demand of urban buildings at city scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2318-2327.
    7. Alkistis E. Kanteraki & Grigorios L. Kyriakopoulos & Miltiadis Zamparas & Vasilis C. Kapsalis & Sofoklis S. Makridis & Giouli Mihalakakou, 2020. "Investigating Thermal Performance of Residential Buildings in Marmari Region, South Evia, Greece," Challenges, MDPI, vol. 11(1), pages 1-22, February.
    8. Jiayu Li & Bohong Zheng & Komi Bernard Bedra & Zhe Li & Xiao Chen, 2021. "Evaluating the Effect of Window-to-Wall Ratios on Cooling-Energy Demand on a Typical Summer Day," IJERPH, MDPI, vol. 18(16), pages 1-13, August.

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