Publication:
Artificial neural network based technique for energy management prediction

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dc.citedby11
dc.contributor.authorWahab N.Ab.en_US
dc.contributor.authorMat Yasin Z.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorAziz N.F.A.en_US
dc.contributor.authorid35790572400en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid57221906825en_US
dc.date.accessioned2023-05-29T07:29:08Z
dc.date.available2023-05-29T07:29:08Z
dc.date.issued2019
dc.description.abstractThe energy management of electrical machine is significant to ensure efficient power consumption. Mismanagement of energy consumption could give impact on low efficiency of energy consumption that leads to power wastage. This paper presents analysis of power consumption and electricity costing of the electrical machineries and equipment in High Voltage (HV) and Electrical Machine (EM) Laboratories at Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia. The electrical data are collected using Fluke Meter 1750. Based on the analysis, it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively. For prediction of energy consumption of the two laboratories, Artificial Neural Network (ANN) algorithm is applied as computational tool using feedforward network type. The results show that the ANN is successfully modelled to predict the energy consumption. Copyright � 2020 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v17.i1.pp94-101
dc.identifier.epage101
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85073811697
dc.identifier.spage94
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073811697&doi=10.11591%2fijeecs.v17.i1.pp94-101&partnerID=40&md5=ccd9b201adb776bb02ec67887cd8b5e1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24936
dc.identifier.volume17
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleArtificial neural network based technique for energy management predictionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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