Energy demand forecasting using fused machine learning approaches

Simple item page
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorNoreen, Sajida
dc.contributor.authorSaid, Raed A.
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorSiddiqui, Shahan Yamin
dc.contributor.authorAbbas, Sagheer
dc.contributor.authorAftab, Shabib
dc.contributor.authorAhmad, Munir
dc.date.accessioned2021-10-13T14:02:24Z
dc.date.available2021-10-13T14:02:24Z
dc.date.copyright© 2022
dc.date.issued2022
dc.description.abstractThe 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.citationGhazal, 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.2022.019658en_US
dc.identifier.issn10798587
dc.identifier.urihttps://doi.org/doi:10.32604/IASC.2022.019658
dc.identifier.urihttp://hdl.handle.net/20.500.12519/446
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relationAuthors 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.ispartofseriesIntelligent Automation and Soft Computing;Volume 31, Issue 1
dc.rightsCreative Commons Attribution 4.0 International License
dc.rights.holderCopyright : © 2022, Tech Science Press. All rights reserved.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDecision-based fusionen_US
dc.subjectDeep extreme learningen_US
dc.subjectEDF-FMLAen_US
dc.subjectEnergyen_US
dc.subjectFeature fusionen_US
dc.subjectSmart metersen_US
dc.subjectSVMen_US
dc.titleEnergy demand forecasting using fused machine learning approachesen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction
Size:
912.83 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

International Business