IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v302y2024ics0360544224015779.html
   My bibliography  Save this article

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

Author

Listed:
  • Wang, Guimei
  • Moayedi, Hossein
  • Thi, Quynh T.
  • Mirzaei, Mojtaba

Abstract

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10 and 100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5 and 1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

Suggested Citation

  • Wang, Guimei & Moayedi, Hossein & Thi, Quynh T. & Mirzaei, Mojtaba, 2024. "Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms," Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:energy:v:302:y:2024:i:c:s0360544224015779
    DOI: 10.1016/j.energy.2024.131804
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224015779
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131804?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Zheng, Shanshan & Hai, Qing & Zhou, Xiao & Stanford, Russell J., 2024. "A novel multi-generation system for sustainable power, heating, cooling, freshwater, and methane production: Thermodynamic, economic, and environmental analysis," Energy, Elsevier, vol. 290(C).
    3. Qiucheng Li & Jiang Hu & Bolin Yu, 2021. "Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China," Energies, MDPI, vol. 14(13), pages 1-17, June.
    4. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    5. Xuan Liu & Zehao Li & Xinyi Fu & Zhengtong Yin & Mingzhe Liu & Lirong Yin & Wenfeng Zheng, 2023. "Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images," Land, MDPI, vol. 12(4), pages 1-21, April.
    6. Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).
    7. Hossein Moayedi & Bao Le Van, 2022. "Feasibility of Harris Hawks Optimization in Combination with Fuzzy Inference System Predicting Heating Load Energy Inside Buildings," Energies, MDPI, vol. 15(23), pages 1-17, December.
    8. Sun, Wei & Liu, Yuduo & Li, Mingyang & Cheng, Qinglin & Zhao, Lixin, 2023. "Study on heat flow transfer characteristics and main influencing factors of waxy crude oil tank during storage heating process under dynamic thermal conditions," Energy, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

    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. Cai, Wei & Wen, Xiaodong & Li, Chaoen & Shao, Jingjing & Xu, Jianguo, 2023. "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, Elsevier, vol. 273(C).
    2. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    3. Qingwen, Wang & XiaoHui, Chu & Chao, Yu, 2024. "Modeling of heat gain through green roofs utilizing artificial intelligence techniques," Energy, Elsevier, vol. 303(C).
    4. Haonan Zhang, 2023. "Leveraging policy instruments and financial incentives to reduce embodied carbon in energy retrofits," Papers 2304.03403, arXiv.org.
    5. Thomas Wu & Bo Wang & Dongdong Zhang & Ziwei Zhao & Hongyu Zhu, 2023. "Benchmarking Evaluation of Building Energy Consumption Based on Data Mining," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    6. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    7. Zhiyong Li & Shiping Pu & Yougen Chen & Renyong Wei, 2020. "An Integration Optimization Strategy of Line Voltage Cascaded Quasi-Z-Source Inverter Parameters Based on GRA-FA," Energies, MDPI, vol. 13(17), pages 1-24, August.
    8. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.
    9. Akinbowale Nathaniel Babatunde & Roseline Oluwaseun Ogundokun & Latifat Bukola Adeoye & Sanjay Misra, 2023. "Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    10. Wang, Chuan'an & Pouramini, Somayeh, 2024. "Multi-objective modified satin Bowerbird optimization algorithm used for simulation-based energy consumption optimization of yearly energy demand of lighting and cooling in a test case room," Energy, Elsevier, vol. 292(C).
    11. Boni Sena & Sheikh Ahmad Zaki & Hom Bahadur Rijal & Jorge Alfredo Ardila-Rey & Nelidya Md Yusoff & Fitri Yakub & Farah Liana & Mohamad Zaki Hassan, 2021. "Development of an Electrical Energy Consumption Model for Malaysian Households, Based on Techno-Socioeconomic Determinant Factors," Sustainability, MDPI, vol. 13(23), pages 1-22, November.
    12. Amira Mouakher & Wissem Inoubli & Chahinez Ounoughi & Andrea Ko, 2022. "Expect : EXplainable Prediction Model for Energy ConsumpTion," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
    13. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
    14. Amini Toosi, Hashem & Del Pero, Claudio & Leonforte, Fabrizio & Lavagna, Monica & Aste, Niccolò, 2023. "Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization," Applied Energy, Elsevier, vol. 334(C).
    15. Li, Na & Dilanchiev, Azer & Mustafa, Ghulam, 2023. "From oil and mineral extraction to renewable energy: Analyzing the efficiency of green technology innovation in the transformation of the oil and gas sector in the extractive industry," Resources Policy, Elsevier, vol. 86(PA).
    16. Salvia, Monica & Simoes, Sofia G. & Herrando, María & Čavar, Marko & Cosmi, Carmelina & Pietrapertosa, Filomena & Gouveia, João Pedro & Fueyo, Norberto & Gómez, Antonio & Papadopoulou, Kiki & Taxeri, , 2021. "Improving policy making and strategic planning competencies of public authorities in the energy management of municipal public buildings: The PrioritEE toolbox and its application in five mediterranea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    17. Pan, Jing & Bai, Jinke & Zhang, Guanghua & Gao, Xiaomin, 2024. "Proposing an innovative polygeneration process based on biogas fuel: A comprehensive thermo-enviro-economic (4E) analysis," Energy, Elsevier, vol. 304(C).
    18. César Augusto Ruiz-Agudelo & Francisco Paula Gutiérrez-Bonilla, 2024. "The remnant natural capital of the Magdalena-Cauca basin: immense losses for the 80% of Colombian inhabitants," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 14(1), pages 135-153, March.
    19. Le, Son Tay & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Teodosio, Birch & Ngo, Tuan Duc, 2024. "Comparative life cycle assessment of renewable energy storage systems for net-zero buildings with varying self-sufficient ratios," Energy, Elsevier, vol. 290(C).
    20. Le, Son Tay & Nguyen, Tuan Ngoc & Bui, Dac-Khuong & Ngo, Tuan Duc, 2024. "Techno-economic and life cycle analysis of renewable energy storage systems in buildings: The effect of uncertainty," Energy, Elsevier, vol. 307(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:eee:energy:v:302:y:2024:i:c:s0360544224015779. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.