Please use this identifier to cite or link to this item: https://repository.sustech.edu/handle/123456789/14696
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dc.contributor.authorTayeb, Eisa Bashier M.
dc.contributor.authorAli, A. Taifour
dc.contributor.authorEmam, Ahmed A.
dc.date.accessioned2016-11-23T08:44:13Z
dc.date.available2016-11-23T08:44:13Z
dc.date.issued2013-04-01
dc.identifier.citationTayeb, Eisa Bashier M.Electrical Energy Management and Load Forecasting in a Smart Grid/Eisa Bashier M. Tayeb;.-Khartoum:Sudan University of Science and Technology,College of Engineering,2013.-4p:ill.- Articleen_US
dc.identifier.urihttp://repository.sustech.edu/handle/123456789/14696
dc.descriptionArticleen_US
dc.description.abstractArtificial Neural Networks (ANN) has been applied to many fields in recent years. Among them, the neural networks with Back Propagation algorithm appear to be most popular and have been widely used in applications such as forecasting and classification problems. This paper presents a study of short-term load forecasting using Artificial Neural Networks (ANNs) and applied it to the Sudan National Electric Company NEC. Neuroshell2 software was used to provide back-propagation neural networks. ANN model used to forecast the load with the performance error as a measure characteristic. The error obtained by comparing the forecasted load data with actual load dataen_US
dc.description.sponsorshipSudan University of Science &Technologyen_US
dc.language.isoenen_US
dc.publisherSudan University of Science and Technologyen_US
dc.subjectDemand Forecastingen_US
dc.subjectEnergy Managementen_US
dc.subjectGeneration Dispatchen_US
dc.subjectNeural Networksen_US
dc.subjectSmart Griden_US
dc.titleElectrical Energy Management and Load Forecasting in a Smart Griden_US
dc.typeArticleen_US
Appears in Collections:College of Engineering

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