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An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Author

Listed:
  • Samira Rastbod

    (Department of Architecture, Abhar Branch, Islamic Azad University, Abhar 4561934367, Iran)

  • Farnaz Rahimi

    (Department of Architecture, Eram Institute of Higher Education, Shiraz 7195746733, Iran)

  • Yara Dehghan

    (Faculty of Architecture and Urban Planning, Department of Architecture, Islamic Azad University of Central Tehran Branch, Tehran 1955847781, Iran)

  • Saeed Kamranfar

    (Department of Architecture, Built Environment and Construction Engineering, Polytechnic Milan, 20133 Milan, Italy)

  • Omrane Benjeddou

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

  • Moncef L. Nehdi

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

Abstract

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (D AN ), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the D AN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear D AN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:231-:d:1012864
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    References listed on IDEAS

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    1. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    2. David Bienvenido-Huertas & Miguel Oliveira & Carlos Rubio-Bellido & David Marín, 2019. "A Comparative Analysis of the International Regulation of Thermal Properties in Building Envelope," Sustainability, MDPI, vol. 11(20), pages 1-30, October.
    3. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    4. Jinmog Han & Jongkyun Bae & Jihoon Jang & Jumi Baek & Seung-Bok Leigh, 2019. "The Derivation of Cooling Set-Point Temperature in an HVAC System, Considering Mean Radiant Temperature," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    5. Ikeda, Shintaro & Nagai, Tatsuo, 2021. "A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems," Applied Energy, Elsevier, vol. 289(C).
    6. 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.
    7. Han, Yongming & Fan, Chenyu & Geng, Zhiqiang & Ma, Bo & Cong, Di & Chen, Kai & Yu, Bin, 2020. "Energy efficient building envelope using novel RBF neural network integrated affinity propagation," Energy, Elsevier, vol. 209(C).
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    1. Saeed Kamranfar & Farid Damirchi & Mitra Pourvaziri & Pardayev Abdunabi Xalikovich & Samira Mahmoudkelayeh & Reza Moezzi & Amir Vadiee, 2023. "A Partial Least Squares Structural Equation Modelling Analysis of the Primary Barriers to Sustainable Construction in Iran," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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