Energy-efficiency model for residential buildings using supervised machine learning algorithm

Simple item page
dc.contributor.authorAslam, Muhammad Shoukat
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorFatima, Areej
dc.contributor.authorSaid, Raed A.
dc.contributor.authorAbbas, Sagheer
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorSiddiqui, Shahan Yamin
dc.contributor.authorAhmad, Munir
dc.date.accessioned2021-09-12T11:12:21Z
dc.date.available2021-09-12T11:12:21Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis 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.2021.017920en_US
dc.description.abstractThe 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.citationAslam, 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/v30n3/44089en_US
dc.identifier.issnVolume 30, Issue 3
dc.identifier.urihttps://doi.org/10.32604/iasc.2021.017920
dc.identifier.urihttp://hdl.handle.net/20.500.12519/438
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relationAuthors 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.ispartofseriesIntelligent Automation and Soft Computing;Volume 30, Issue 3
dc.rightsCreative Commons Attribution 4.0 International License
dc.rights.holderCopyright : © 2021, Tech Science Press. All rights reserved.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGradient descent optimizationen_US
dc.subjectHeating-load predictionen_US
dc.subjectMachine learningen_US
dc.titleEnergy-efficiency model for residential buildings using supervised machine learning algorithmen_US
dc.typeArticleen_US

Files

Collections

International Business