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The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption

Author

Listed:
  • Aiman Albatayneh

    (School of Natural Resources Engineering and Management, German Jordanian University, P.O. Box 35247, Amman 11180, Jordan)

  • Dariusz Alterman

    (Priority Research Centre for Frontier Energy Technologies and Utilisation, The University of Newcastle, Callaghan, NSW 2308, Australia)

  • Adrian Page

    (Priority Research Centre for Frontier Energy Technologies and Utilisation, The University of Newcastle, Callaghan, NSW 2308, Australia)

  • Behdad Moghtaderi

    (Priority Research Centre for Frontier Energy Technologies and Utilisation, The University of Newcastle, Callaghan, NSW 2308, Australia)

Abstract

Building energy assessment software/programs use various assumptions and types of thermal comfort models to forecast energy consumption. This paper compares the results of using two major thermal comfort models (adaptive thermal comfort and the predicted mean vote (PMV) adjusted by the expectancy factor) to examine their influence on the prediction of the energy consumption for several full-scale housing experimental modules constructed on the campus of the University of Newcastle, Australia. Four test modules integrating a variety of walling types (insulated cavity brick (InsCB), cavity brick (CB), insulated reverse brick veneer (InsRBV), and insulated brick veneer (InsBV)) were used for comparing the time necessary for cooling and heating to maintain internal thermal comfort for both models. This research paper exhibits the benefits of adopting the adaptive thermal model for building structures. It shows the effectiveness of this model in helping to reduce energy consumption, increasing the thermal comfort level for the buildings, and therefore reducing greenhouse emissions.

Suggested Citation

  • Aiman Albatayneh & Dariusz Alterman & Adrian Page & Behdad Moghtaderi, 2018. "The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3609-:d:174594
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    References listed on IDEAS

    as
    1. Ren, Zhengen & Chen, Dong, 2018. "Modelling study of the impact of thermal comfort criteria on housing energy use in Australia," Applied Energy, Elsevier, vol. 210(C), pages 152-166.
    2. Eva Maleviti & Walter Wehrmeyer & Yacob Mulugetta, 2013. "An Empirical Assessment to Express the Variability of Buildings' Energy Consumption," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 2(3), pages 55-67, July.
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    Citations

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    Cited by:

    1. Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
    2. Aiman Albatayneh & Mohammed N. Assaf & Renad Albadaineh & Adel Juaidi & Ramez Abdallah & Alberto Zabalo & Francisco Manzano-Agugliaro, 2022. "Reducing the Operating Energy of Buildings in Arid Climates through an Adaptive Approach," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    3. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    4. Kashif Irshad & Salem Algarni & Mohammad Tauheed Ahmad & Sayed Ameenuddin Irfan & Khairul Habib & Mostafa A.H. Abdelmohimen & Md. Hasan Zahir & Gulam Mohammed Sayeed Ahmed, 2019. "Microclimate Thermal Management Using Thermoelectric Air-Cooling Duct System Operated at Five Incremental Powers and its Effect on Sleep Adaptation of the Occupants," Energies, MDPI, vol. 12(19), pages 1-25, September.
    5. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
    6. Aiman Albatayneh & Dariusz Alterman & Adrian Page & Behdad Moghtaderi, 2019. "The Significance of the Adaptive Thermal Comfort Limits on the Air-Conditioning Loads in a Temperate Climate," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    7. Pao-Hung Lin & Chin-Chuan Chang & Yu-Hui Lin & Wei-Liang Lin, 2019. "Green BIM Assessment Applying for Energy Consumption and Comfort in the Traditional Public Market: A Case Study," Sustainability, MDPI, vol. 11(17), pages 1-26, August.
    8. Reihaneh Aram & Halil Zafer Alibaba, 2019. "Analyzing Atrium Volume Designs for Hot and Humid Climates," Sustainability, MDPI, vol. 11(22), pages 1-40, November.
    9. Mohamed H. Elnabawi & Esmail Saber, 2022. "Reducing carbon footprint and cooling demand in arid climates using an integrated hybrid ventilation and photovoltaic approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3396-3418, March.
    10. Hermawan Hermawan & Jozef Švajlenka, 2021. "The Connection between Architectural Elements and Adaptive Thermal Comfort of Tropical Vernacular Houses in Mountain and Beach Locations," Energies, MDPI, vol. 14(21), pages 1-19, November.
    11. Wen Cao & Lin Yang & Qinyi Zhang & Lihua Chen & Weidong Wu, 2021. "Evaluation of Rural Dwellings’ Energy-Saving Retrofit with Adaptive Thermal Comfort Theory," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    12. Aiman Albatayneh & Adel Juaidi & Ramez Abdallah & Francisco Manzano-Agugliaro, 2021. "Influence of the Advancement in the LED Lighting Technologies on the Optimum Windows-to-Wall Ratio of Jordanians Residential Buildings," Energies, MDPI, vol. 14(17), pages 1-20, September.

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