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New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

Author

Listed:
  • Nadia Jahanafroozi

    (Department of Architecture College of Design, North Carolina State University, Raleigh, NC 27695, USA)

  • Saman Shokrpour

    (Faculty of Architecture & Urbanism, Tabriz Islamic Art University (TIAU), Tabriz 5164736931, Iran)

  • Fatemeh Nejati

    (Department of Art and Architecture, Faculty of Architecture, Khatam University, Tehran 1991813741, Iran)

  • Omrane Benjeddou

    (Civil Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj 16273, Saudi Arabia)

  • Mohammad Worya Khordehbinan

    (Cultur & Art Applied Scientific Teaching Center Kurdistan Branch, University of Applied Science and Technology, Sanandaj 6618758671, Iran)

  • Afshin Marani

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

  • Moncef L. Nehdi

    (Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada)

Abstract

Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load ( L C ) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the L C pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

Suggested Citation

  • Nadia Jahanafroozi & Saman Shokrpour & Fatemeh Nejati & Omrane Benjeddou & Mohammad Worya Khordehbinan & Afshin Marani & Moncef L. Nehdi, 2022. "New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14446-:d:962559
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    References listed on IDEAS

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    Cited by:

    1. Arash Mohammadi Fallah & Ehsan Ghafourian & Ladan Shahzamani Sichani & Hossein Ghafourian & Behdad Arandian & Moncef L. Nehdi, 2023. "Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    2. Samira Rastbod & Farnaz Rahimi & Yara Dehghan & Saeed Kamranfar & Omrane Benjeddou & Moncef L. Nehdi, 2022. "An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings," Sustainability, MDPI, vol. 15(1), pages 1-15, December.

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