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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
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    References listed on IDEAS

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    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.

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