IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i15p8298-d601012.html
   My bibliography  Save this article

Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

Author

Listed:
  • Ahmed Salih Mohammed

    (Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah 46001, Iraq)

  • Panagiotis G. Asteris

    (Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece)

  • Mohammadreza Koopialipoor

    (Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran)

  • Dimitrios E. Alexakis

    (Laboratory of Geoenvironmental Science and Environmental Quality Assurance, Department of Civil Engineering, University of West Attica, 12241 Athens, Greece)

  • Minas E. Lemonis

    (Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, 14121 Athens, Greece)

  • Danial Jahed Armaghani

    (Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, Russia)

Abstract

In this research, a new machine-learning approach was proposed to evaluate the effects of eight input parameters (surface area, relative compactness, wall area, overall height, roof area, orientation, glazing area distribution, and glazing area) on two output parameters, namely, heating load (HL) and cooling load (CL), of the residential buildings. The association strength of each input parameter with each output was systematically investigated using a variety of basic statistical analysis tools to identify the most effective and important input variables. Then, different combinations of data were designed using the intelligent systems, and the best combination was selected, which included the most optimal input data for the development of stacking models. After that, various machine learning models, i.e., XGBoost, random forest, classification and regression tree, and M5 tree model, were applied and developed to predict HL and CL values of the energy performance of buildings. The mentioned techniques were also used as base techniques in the forms of stacking models. As a result, the XGboost-based model achieved a higher accuracy level (HL: coefficient of determination, R 2 = 0.998; CL: R 2 = 0.971) with a lower system error (HL: root mean square error, RMSE = 0.461; CL: RMSE = 1.607) than the other developed models in predicting both HL and CL values. Using new stacking-based techniques, this research was able to provide alternative solutions for predicting HL and CL parameters with appropriate accuracy and runtime.

Suggested Citation

  • Ahmed Salih Mohammed & Panagiotis G. Asteris & Mohammadreza Koopialipoor & Dimitrios E. Alexakis & Minas E. Lemonis & Danial Jahed Armaghani, 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8298-:d:601012
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/15/8298/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/15/8298/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jian Zhou & Xibing Li & Hani Mitri, 2015. "Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 79(1), pages 291-316, October.
    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. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    4. Yao, Runming & Li, Baizhan & Steemers, Koen, 2005. "Energy policy and standard for built environment in China," Renewable Energy, Elsevier, vol. 30(13), pages 1973-1988.
    5. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    6. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
    7. Pilechiha, Peiman & Mahdavinejad, Mohammadjavad & Pour Rahimian, Farzad & Carnemolla, Phillippa & Seyedzadeh, Saleh, 2020. "Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency," Applied Energy, Elsevier, vol. 261(C).
    8. Cai, W.G. & Wu, Y. & Zhong, Y. & Ren, H., 2009. "China building energy consumption: Situation, challenges and corresponding measures," Energy Policy, Elsevier, vol. 37(6), pages 2054-2059, June.
    9. Yaolin Lin & Shiquan Zhou & Wei Yang & Long Shi & Chun-Qing Li, 2018. "Development of Building Thermal Load and Discomfort Degree Hour Prediction Models Using Data Mining Approaches," Energies, MDPI, vol. 11(6), pages 1-14, June.
    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. Chia Yu Huat & Seyed Mohammad Hossein Moosavi & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Masoud Monjezi & Sai Hin Lai, 2021. "Factors Influencing Pile Friction Bearing Capacity: Proposing a Novel Procedure Based on Gradient Boosted Tree Technique," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    2. Qun Yu & Masoud Monjezi & Ahmed Salih Mohammed & Hesam Dehghani & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh, 2021. "Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation," Sustainability, MDPI, vol. 13(22), pages 1-15, November.
    3. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    4. Yan Li & Fathin Nur Syakirah Hishamuddin & Ahmed Salih Mohammed & Danial Jahed Armaghani & Dmitrii Vladimirovich Ulrikh & Ali Dehghanbanadaki & Aydin Azizi, 2021. "The Effects of Rock Index Tests on Prediction of Tensile Strength of Granitic Samples: A Neuro-Fuzzy Intelligent System," Sustainability, MDPI, vol. 13(19), pages 1-21, September.

    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. Bai, Lujian & Yang, Liu & Song, Bing & Liu, Na, 2020. "A new approach to develop a climate classification for building energy efficiency addressing Chinese climate characteristics," Energy, Elsevier, vol. 195(C).
    2. Zhang, Lin, 2013. "Model projections and policy reviews for energy saving in China's service sector," Energy Policy, Elsevier, vol. 59(C), pages 312-320.
    3. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
    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. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
    6. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
    7. Zheng, Yingmei & Qi, Jianhong & Chen, Xiaoliang, 2011. "The effect of increasing exports on industrial energy intensity in China," Energy Policy, Elsevier, vol. 39(5), pages 2688-2698, May.
    8. Zongguo Zhang & Xianyang Qiu & Xiuzhi Shi & Zhi Yu, 2023. "Chamber roof deformation prediction and analysis of underground mining using experimental design methodologies," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 757-777, January.
    9. 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).
    10. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
    11. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Optimization design of BCHP system to maximize to save energy and reduce environmental impact," Energy, Elsevier, vol. 35(8), pages 3388-3398.
    12. Liu, Wenling & Zhang, Jinyun & Bluemling, Bettina & Mol, Arthur P.J. & Wang, Can, 2015. "Public participation in energy saving retrofitting of residential buildings in China," Applied Energy, Elsevier, vol. 147(C), pages 287-296.
    13. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    14. 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).
    15. Yang, Li & He, Bao-jie & Ye, Miao, 2014. "The application of solar technologies in building energy efficiency: BISE design in solar-powered residential buildings," Technology in Society, Elsevier, vol. 38(C), pages 111-118.
    16. Lam, Joseph C. & Wan, Kevin K.W. & Lau, Chris C.S. & Yang, Liu, 2008. "Climatic influences on solar modelling in China," Renewable Energy, Elsevier, vol. 33(7), pages 1591-1604.
    17. Wei Wei & Ling-Yun He, 2017. "China Building Energy Consumption: Definitions and Measures from an Operational Perspective," Energies, MDPI, vol. 10(5), pages 1-16, April.
    18. Zhang, Li & Wu, Jing & Liu, Hongyu, 2018. "Policies to enhance the drivers of green housing development in China," Energy Policy, Elsevier, vol. 121(C), pages 225-235.
    19. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    20. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.

    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:13:y:2021:i:15:p:8298-:d:601012. 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.