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 not available at CUD collection. The version of scholarly record of this article is published in Intelligent Automation and Soft Computing (2021), available online at: 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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