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Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm

Muhammad Shoukat Aslam1, Taher M. Ghazal2,3, Areej Fatima4, Raed A. Said5, Sagheer Abbas1, Muhammad Adnan Khan6,7,*, Shahan Yamin Siddiqui1,8, Munir Ahmad1

1 School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
2 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia
3 School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE
4 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
5 Canadian University Dubai, Dubai, UAE
6 Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore, 54000, Pakistan
7 Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Seongnam, 13557, Korea
8 School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan

* Corresponding Author: Muhammad Adnan Khan. Email: email

Intelligent Automation & Soft Computing 2021, 30(3), 881-888.


The real-time management and control of heating-system networks in residential buildings has tremendous energy-saving potential, and accurate load prediction is the basis for system monitoring. In this regard, selecting the appropriate input parameters is the key to accurate heating-load forecasting. In existing models for forecasting heating loads and selecting input parameters, with an increase in the length of the prediction cycle, the heating-load rate gradually decreases, and the influence of the outside temperature gradually increases. In view of different types of solutions for improving buildings’ energy efficiency, this study proposed a Energy-efficiency model for residential buildings based on gradient descent optimization (E2B-GDO). This model can predict a building’s heating-load conservation based on a building energy performance dataset. The input layer includes area (distribution of the glazing area, wall area, and surface area), relative density, and overall elevation. The proposed E2B-GDO model achieved an accuracy of 99.98% for training and 98.00% for validation.


Cite This Article

APA Style
Aslam, M.S., Ghazal, T.M., Fatima, A., Said, R.A., Abbas, S. et al. (2021). Energy-efficiency model for residential buildings using supervised machine learning algorithm. Intelligent Automation & Soft Computing, 30(3), 881-888.
Vancouver Style
Aslam MS, Ghazal TM, Fatima A, Said RA, Abbas S, Khan MA, et al. Energy-efficiency model for residential buildings using supervised machine learning algorithm. Intell Automat Soft Comput . 2021;30(3):881-888
IEEE Style
M.S. Aslam et al., "Energy-Efficiency Model for Residential Buildings Using Supervised Machine Learning Algorithm," Intell. Automat. Soft Comput. , vol. 30, no. 3, pp. 881-888. 2021.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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