Energy-efficiency model for residential buildings using supervised machine learning algorithm
dc.contributor.author | Aslam, Muhammad Shoukat | |
dc.contributor.author | Ghazal, Taher M. | |
dc.contributor.author | Fatima, Areej | |
dc.contributor.author | Said, Raed A. | |
dc.contributor.author | Abbas, Sagheer | |
dc.contributor.author | Khan, Muhammad Adnan | |
dc.contributor.author | Siddiqui, Shahan Yamin | |
dc.contributor.author | Ahmad, Munir | |
dc.date.accessioned | 2021-09-12T11:12:21Z | |
dc.date.available | 2021-09-12T11:12:21Z | |
dc.date.copyright | © 2021 | |
dc.date.issued | 2021 | |
dc.description | This article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Intelligent Automation and Soft Computing (2021), accessible online through this link https://doi.org/10.32604/iasc.2021.017920 | en_US |
dc.description.abstract | 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 appro-priate 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. © 2021, Tech Science Press. All rights reserved. | en_US |
dc.identifier.citation | Aslam, M. S., Ghazal, T. M., Fatima, A., Said, R. A., Abbas, S., Khan, M. A., . . . Ahmad, M. (2021). Energy-efficiency model for residential buildings using supervised machine learning algorithm. Intelligent Automation and Soft Computing, 30(3), 881-888. https://www.techscience.com/iasc/v30n3/44089 | en_US |
dc.identifier.issn | Volume 30, Issue 3 | |
dc.identifier.uri | https://doi.org/10.32604/iasc.2021.017920 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12519/438 | |
dc.language.iso | en | en_US |
dc.publisher | Tech Science Press | en_US |
dc.relation | Authors Affiliations : Aslam, M.S., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan; Ghazal, T.M., Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), Bangi, Selangor, 43600, Malaysia, School of Information Technology, Skyline University College, University City Sharjah, Sharjah, 1797, United Arab Emirates; Fatima, A., Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan; Said, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Abbas, S., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan; Khan, M.A., Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore, 54000, Pakistan, Pattern Recognition and Machine Learning Lab, Department of Software Engineering, Gachon University, Seongnam, 13557, South Korea; Siddiqui, S.Y., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan, School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan; Ahmad, M., School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan | |
dc.relation.ispartofseries | Intelligent Automation and Soft Computing;Volume 30, Issue 3 | |
dc.rights | Creative Commons Attribution 4.0 International License | |
dc.rights.holder | Copyright : © 2021, Tech Science Press. All rights reserved. | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Gradient descent optimization | en_US |
dc.subject | Heating-load prediction | en_US |
dc.subject | Machine learning | en_US |
dc.title | Energy-efficiency model for residential buildings using supervised machine learning algorithm | en_US |
dc.type | Article | en_US |
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