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Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms

Author

Listed:
  • Hamzah Ali Alkhazaleh

    (College of Engineering and IT, University of Dubai, Academic City, Dubai 14143, United Arab Emirates)

  • Navid Nahi

    (Department of Architecture, Islamic Azad University, Tehran Science and Research Branch (East Azerbaijan), Tehran 14778-93855, Iran)

  • Mohammad Hossein Hashemian

    (Department of Architecture, Tehran University, Kish Campus, Kish 13114-16846, Iran)

  • Zohreh Nazem

    (Department of Architecture and Urban Design, Islamic Azad University Qazvin Branch, Qazvin 34185-1416, Iran)

  • Wameed Deyah Shamsi

    (Information Technology Unit, Al-Mustaqbal University College, Babylon 51001, Iraq)

  • Moncef L. Nehdi

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

Abstract

Increasing consumption of energy calls for proper approximation of demand towards a sustainable and cost-effective development. In this work, novel hybrid methodologies aim to predict the annual thermal energy demand (ATED) by analyzing the characteristics of the building, such as transmission coefficients of the elements, glazing, and air-change conditions. For this objective, an adaptive neuro-fuzzy-inference system (ANFIS) was optimized with equilibrium optimization (EO) and Harris hawks optimization (HHO) to provide a globally optimum training. Moreover, these algorithms were compared to two benchmark techniques, namely grey wolf optimizer (GWO) and slap swarm algorithm (SSA). The performance of the designed hybrids was evaluated using different accuracy indicators, and based on the results, ANFIS-EO and ANFIS-HHO (with respective RMSEs equal to 6.43 and 6.90 kWh·m −2 ·year −1 versus 9.01 kWh·m −2 ·year −1 for ANFIS-GWO and 11.80 kWh·m −2 ·year −1 for ANFIS-SSA) presented the most accurate analysis of the ATED. Hence, these models are recommended for practical usages, i.e., the early estimations of ATED, leading to a more efficient design of buildings.

Suggested Citation

  • Hamzah Ali Alkhazaleh & Navid Nahi & Mohammad Hossein Hashemian & Zohreh Nazem & Wameed Deyah Shamsi & Moncef L. Nehdi, 2022. "Prediction of Thermal Energy Demand Using Fuzzy-Based Models Synthesized with Metaheuristic Algorithms," Sustainability, MDPI, vol. 14(21), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14385-:d:962120
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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. Wang, Guimei & Mukhtar, Azfarizal & Moayedi, Hossein & Khalilpoor, Nima & Tt, Quynh, 2024. "Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector," Energy, Elsevier, vol. 298(C).

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