IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14446-d962559.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14446/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14446/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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).
    4. repec:zib:zbesmy:v:3:y:2022:i:2:p:81-84 is not listed on IDEAS
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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).
    10. 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.
    11. 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).
    12. Xie, Xiangmin & Chen, Daolian, 2022. "Data-driven dynamic harmonic model for modern household appliances," Applied Energy, Elsevier, vol. 312(C).
    13. 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).
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fahad R. Albogamy, 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid," Energies, MDPI, vol. 15(21), pages 1-31, October.
    2. Jawed Mustafa & Fahad Awjah Almehmadi & Saeed Alqaed & Mohsen Sharifpur, 2022. "Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    3. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    4. Hossein Moayedi & Bao Le Van, 2022. "The Applicability of Biogeography-Based Optimization and Earthworm Optimization Algorithm Hybridized with ANFIS as Reliable Solutions in Estimation of Cooling Load in Buildings," Energies, MDPI, vol. 15(19), pages 1-17, October.
    5. Yan, Xiuying & Ji, Xingxing & Meng, Qinglong & Sun, Hang & Lei, Yu, 2024. "A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism," Energy, Elsevier, vol. 292(C).
    6. Taimoor Ahmad Khan & Amjad Ullah & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Faheem Ali & Sajjad Ali & Sheraz Khan & Khalid Rehman, 2022. "A Fractional Order Super Twisting Sliding Mode Controller for Energy Management in Smart Microgrid Using Dynamic Pricing Approach," Energies, MDPI, vol. 15(23), pages 1-14, November.
    7. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    8. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
    9. Liu, Yiren & Zhao, Xiangyu & Qin, S. Joe, 2024. "Dynamically engineered multi-modal feature learning for predictions of office building cooling loads," Applied Energy, Elsevier, vol. 355(C).
    10. Kalim Ullah & Taimoor Ahmad Khan & Ghulam Hafeez & Imran Khan & Sadia Murawwat & Basem Alamri & Faheem Ali & Sajjad Ali & Sheraz Khan, 2022. "Demand Side Management Strategy for Multi-Objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid," Energies, MDPI, vol. 15(19), pages 1-14, September.
    11. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).
    12. Loke Kok Foong & Binh Nguyen Le, 2022. "Teaching–Learning–Based Optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads," Energies, MDPI, vol. 15(21), pages 1-20, November.
    13. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    14. 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.
    15. Hua, Pengmin & Wang, Haichao & Xie, Zichan & Lahdelma, Risto, 2024. "District heating load patterns and short-term forecasting for buildings and city level," Energy, Elsevier, vol. 289(C).
    16. Zhou, Jincheng & Hai, Tao & Ali, Masood Ashraf & Shamseldin, Mohamed A. & Almojil, Sattam Fahad & Almohana, Abdulaziz Ibrahim & Alali, Abdulrhman Fahmi, 2023. "Waste heat recovery of a wind turbine for poly-generation purpose: Feasibility analysis, environmental impact assessment, and parametric optimization," Energy, Elsevier, vol. 263(PD).
    17. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    18. Chen, Zhiwen & Deng, Qiao & Ren, Hao & Zhao, Zhengrun & Peng, Tao & Yang, Chunhua & Gui, Weihua, 2022. "A new energy consumption prediction method for chillers based on GraphSAGE by combining empirical knowledge and operating data," Applied Energy, Elsevier, vol. 310(C).
    19. Liu, Zeyu & Li, Hang & Hou, Kai & Xu, Xiandong & Jia, Hongjie & Zhu, Lewei & Mu, Yunfei, 2023. "Risk assessment and alleviation of regional integrated energy system considering cross-system failures," Applied Energy, Elsevier, vol. 350(C).
    20. Xing, Zhuoqun & Pan, Yiqun & Yang, Yiting & Yuan, Xiaolei & Liang, Yumin & Huang, Zhizhong, 2024. "Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction," Applied Energy, Elsevier, vol. 365(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14446-:d:962559. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.