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Office building cooling load reduction using thermal analysis method – A case study

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  • Wan Mohd Nazi, Wan Iman
  • Royapoor, Mohammad
  • Wang, Yaodong
  • Roskilly, Anthony Paul

Abstract

Buildings worldwide consume approximately 45% of primary energy sources, making it the single largest energy consumption sector. The importance of improving a building’s energy performance was emphasized by the government with the enforcement of sustainable building policies. Article 9 of the Directive 2010/31/EU of the European Parliament and the Council (19th May 2010) on the energy performance of buildings states the importance of stimulating refurbishment of existing buildings into near zero-energy buildings. However, the effectiveness of the process depends on the basic building structure and the refurbishment designs. Hence, methods to find the effective strategies for retrofitting and modelling to predict energy reduction is vital. Unlike the previous studies, this paper presents a method for a deep building retrofit based on the whole building’s thermal analysis specifically for cooling demand countries. This work set against recommended best practice office building energy benchmarks in Malaysia, and following a comprehensive building audit, a retrofit strategy was proposed based on target building’s thermal analysis with cooling demand reduction in particular focus. It was found that 71% of the building’s heat gain emanated from its lighting system and solar heat gain through windows. A 40.2% reduction in the building’s cooling load is estimated to reduce 47% of the total energy consumption. A comparison of the actual and simulated energy results suggested that the simulation made under predicted the energy reduction by 4.3%.

Suggested Citation

  • Wan Mohd Nazi, Wan Iman & Royapoor, Mohammad & Wang, Yaodong & Roskilly, Anthony Paul, 2017. "Office building cooling load reduction using thermal analysis method – A case study," Applied Energy, Elsevier, vol. 185(P2), pages 1574-1584.
  • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1574-1584
    DOI: 10.1016/j.apenergy.2015.12.053
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    References listed on IDEAS

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    Cited by:

    1. Jing Zhao & Yaoqi Duan & Xiaojuan Liu, 2018. "Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    2. Pop, Octavian G. & Fechete Tutunaru, Lucian & Bode, Florin & Abrudan, Ancuţa C. & Balan, Mugur C., 2018. "Energy efficiency of PCM integrated in fresh air cooling systems in different climatic conditions," Applied Energy, Elsevier, vol. 212(C), pages 976-996.
    3. Ana Paola Vargas & Leon Hamui, 2021. "Thermal Energy Performance Simulation of a Residential Building Retrofitted with Passive Design Strategies: A Case Study in Mexico," Sustainability, MDPI, vol. 13(14), pages 1-21, July.
    4. Javanroodi, Kavan & Mahdavinejad, Mohammadjavad & Nik, Vahid M., 2018. "Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate," Applied Energy, Elsevier, vol. 231(C), pages 714-746.
    5. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.

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