Energy demand forecasting using fused machine learning approaches

dc.contributor.author Ghazal, Taher M.
dc.contributor.author Noreen, Sajida
dc.contributor.author Said, Raed A.
dc.contributor.author Khan, Muhammad Adnan
dc.contributor.author Siddiqui, Shahan Yamin
dc.contributor.author Abbas, Sagheer
dc.contributor.author Aftab, Shabib
dc.contributor.author Ahmad, Munir
dc.date.accessioned 2021-10-13T14:02:24Z
dc.date.available 2021-10-13T14:02:24Z
dc.date.copyright © 2022
dc.date.issued 2022
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 (2022), available online at: https://doi.org/doi:10.32604/IASC.2022.019658 en_US
dc.description.abstract The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, trans-mission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches. © 2022, Tech Science Press. All rights reserved. en_US
dc.identifier.citation Ghazal, T. M., Noreen, S., Said, R. A., Khan, M. A., Siddiqui, S. Y., Abbas, S., . . . Ahmad, M. (2022). Energy demand forecasting using fused machine learning approaches. Intelligent Automation and Soft Computing, 31(1), 539-553. https://doi.org/doi:10.32604/IASC.2022.019658 en_US
dc.identifier.issn 10798587
dc.identifier.uri https://doi.org/doi:10.32604/IASC.2022.019658
dc.identifier.uri http://hdl.handle.net/20.500.12519/446
dc.language.iso en en_US
dc.publisher Tech Science Press en_US
dc.relation Authors Affiliations : 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; Noreen, S., School of Computer Science, NCBA&E, Lahore, 54000, Pakistan; Said, R.A., Canadian University Dubai, Dubai, United Arab Emirates; Khan, M.A., Pattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam, 13557, South Korea; Siddiqui, S.Y., School of Computer Science, NCBA&E, Lahore, 54000, Pakistan, School of Computer Science, Minhaj University Lahore, Lahore, 54000, Pakistan; Abbas, S., School of Computer Science, NCBA&E, Lahore, 54000, Pakistan; Aftab, S., School of Computer Science, NCBA&E, Lahore, 54000, Pakistan; Ahmad, M., School of Computer Science, NCBA&E, Lahore, 54000, Pakistan
dc.relation.ispartofseries Intelligent Automation and Soft Computing;Volume 31, Issue 1
dc.rights Creative Commons Attribution 4.0 International License
dc.rights.holder Copyright : © 2022, Tech Science Press. All rights reserved.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Decision-based fusion en_US
dc.subject Deep extreme learning en_US
dc.subject EDF-FMLA en_US
dc.subject Energy en_US
dc.subject Feature fusion en_US
dc.subject Smart meters en_US
dc.subject SVM en_US
dc.title Energy demand forecasting using fused machine learning approaches en_US
dc.type Article en_US
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