Please use this identifier to cite or link to this item: http://dspace.uniten.edu.my/jspui/handle/123456789/15163
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dc.contributor.authorN. Ab. Wahaben_US
dc.contributor.authorZ. Mat Yasinen_US
dc.contributor.authorN. A. Salimen_US
dc.contributor.authorN. F. A. Azizen_US
dc.date.accessioned2020-08-24T02:58:36Z-
dc.date.available2020-08-24T02:58:36Z-
dc.date.issued2020-01-
dc.identifier.urihttp://dspace.uniten.edu.my/jspui/handle/123456789/15163-
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.en_US
dc.language.isoenen_US
dc.relation.ispartofIndonesian Journal of Electrical Engineering and Computer Scienceen_US
dc.subjectArtifical neural networken_US
dc.subjectElectricity costen_US
dc.subjectEnergy consumptionen_US
dc.subjectEnergy managementen_US
dc.subjectEnergy predictionen_US
dc.titleArtificial neural network based technique for energy management predictionen_US
dc.typeArticleen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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