Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings

Ahmed Salih Mohammed, Panagiotis G. Asteris, Mohammadreza Koopialipoor, Dimitrios E. Alexakis, Minas E. Lemonis and Danial Jahed Armaghani
Additional contact information
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

Sustainability, 2021, vol. 13, issue 15, 1-22

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.

Keywords: heating load; cooling load; residential building; machine learning; stacking ensemble tree model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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