IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14385-d962120.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14385/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14385/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiao, Yiyong & Zhang, Yue & Kaku, Ikou & Kang, Rui & Pan, Xing, 2021. "Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
    3. Zayed, Mohamed E. & Zhao, Jun & Li, Wenjia & Elsheikh, Ammar H. & Elaziz, Mohamed Abd, 2021. "A hybrid adaptive neuro-fuzzy inference system integrated with equilibrium optimizer algorithm for predicting the energetic performance of solar dish collector," Energy, Elsevier, vol. 235(C).
    4. Jens Baetens & Greet Van Eetvelde & Gert Lemmens & Nezmin Kayedpour & Jeroen D. M. De Kooning & Lieven Vandevelde, 2019. "Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model," Energies, MDPI, vol. 12(13), pages 1-17, July.
    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. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
    7. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    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. 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).
    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. Li, Shunxi & Su, Bowen & St-Pierre, David L. & Sui, Pang-Chieh & Zhang, Guofang & Xiao, Jinsheng, 2017. "Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting," Energy, Elsevier, vol. 140(P1), pages 11-17.
    2. 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.
    3. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    4. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    5. Rao, Congjun & Zhang, Yue & Wen, Jianghui & Xiao, Xinping & Goh, Mark, 2023. "Energy demand forecasting in China: A support vector regression-compositional data second exponential smoothing model," Energy, Elsevier, vol. 263(PC).
    6. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    7. Mahmoudan, Alireza & Samadof, Parviz & Hosseinzadeh, Siamak & Garcia, Davide Astiaso, 2021. "A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization," Energy, Elsevier, vol. 233(C).
    8. Gitae Kim, 2024. "Electric Vehicle Routing Problem with States of Charging Stations," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    9. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    10. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    11. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Konstantinos Sofias & Zoe Kanetaki & Constantinos Stergiou & Sébastien Jacques, 2023. "Combining CAD Modeling and Simulation of Energy Performance Data for the Retrofit of Public Buildings," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    13. Maksymilian Mądziel & Tiziana Campisi, 2024. "Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily," Energies, MDPI, vol. 17(19), pages 1-19, September.
    14. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    15. Tomoya Uenaga & Pedram Omidian & Riya Catherine George & Mohsen Mirzajani & Naser Khaji, 2023. "Seismic Resilience Assessment of Curved Reinforced Concrete Bridge Piers through Seismic Fragility Curves Considering Short- and Long-Period Earthquakes," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    16. Sina Abbasi & Maryam Moosivand & Ilias Vlachos & Mohammad Talooni, 2023. "Designing the Location–Routing Problem for a Cold Supply Chain Considering the COVID-19 Disaster," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    17. Yong Wang & Jingxin Zhou & Yaoyao Sun & Xiuwen Wang & Jiayi Zhe & Haizhong Wang, 2022. "Electric Vehicle Charging Station Location-Routing Problem with Time Windows and Resource Sharing," Sustainability, MDPI, vol. 14(18), pages 1-31, September.
    18. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
    19. 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.
    20. Pedro Paulo Fernandes da Silva & Alberto Hernandez Neto & Ildo Luis Sauer, 2021. "Evaluation of Model Calibration Method for Simulation Performance of a Public Hospital in Brazil," Energies, MDPI, vol. 14(13), pages 1-20, June.

    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:14385-:d:962120. 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.