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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

    as
    1. Yinghao Zhao & Hesong Hu & Lunhua Bai & Mengxiong Tang & Hang Chen & Dingli Su, 2021. "Fragility Analyses of Bridge Structures Using the Logarithmic Piecewise Function-Based Probabilistic Seismic Demand Model," Sustainability, MDPI, vol. 13(14), pages 1-23, July.
    2. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    3. Li, Hang & Hou, Kai & Xu, Xiandong & Jia, Hongjie & Zhu, Lewei & Mu, Yunfei, 2022. "Probabilistic energy flow calculation for regional integrated energy system considering cross-system failures," Applied Energy, Elsevier, vol. 308(C).
    4. Anjan Dutta & Tomal Deb & Shrikant Pathak, 2021. "Automated Data Harmonization (ADH) using Artificial Intelligence (AI)," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 257-275, June.
    5. Xie, Xiangmin & Chen, Daolian, 2022. "Data-driven dynamic harmonic model for modern household appliances," Applied Energy, Elsevier, vol. 312(C).
    6. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    7. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    8. Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    9. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
    10. Xu, Yuanjin & Li, Fei & Asgari, Armin, 2022. "Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms," Energy, Elsevier, vol. 240(C).
    11. Nesticò, Antonio & Elia, Cristina & Naddeo, Vincenzo, 2020. "Sustainability of urban regeneration projects: Novel selection model based on analytic network process and zero-one goal programming," Land Use Policy, Elsevier, vol. 99(C).
    12. Li, Ao & Xiao, Fu & Zhang, Chong & Fan, Cheng, 2021. "Attention-based interpretable neural network for building cooling load prediction," Applied Energy, Elsevier, vol. 299(C).
    13. repec:zib:zbesmy:v:3:y:2022:i:2:p:81-84 is not listed on IDEAS
    14. Wang, Han & Hou, Kai & Zhao, Junbo & Yu, Xiaodan & Jia, Hongjie & Mu, Yunfei, 2022. "Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters," Applied Energy, Elsevier, vol. 315(C).
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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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