Electrical Energy Management and Load Forecasting in a Smart Grid

Tayeb, Eisa Bashier M.; Ali, A. Taifour; Emam, Ahmed A.

dc.contributor.author	Tayeb, Eisa Bashier M.
dc.contributor.author	Ali, A. Taifour
dc.contributor.author	Emam, Ahmed A.
dc.date.accessioned	2016-11-23T08:44:13Z
dc.date.available	2016-11-23T08:44:13Z
dc.date.issued	2013-04-01
dc.identifier.citation	Tayeb, 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.- Article	en_US
dc.identifier.uri	http://repository.sustech.edu/handle/123456789/14696
dc.description	Article	en_US
dc.description.abstract	Artificial 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 data	en_US
dc.description.sponsorship	Sudan University of Science &Technology	en_US
dc.language.iso	en	en_US
dc.publisher	Sudan University of Science and Technology	en_US
dc.subject	Demand Forecasting	en_US
dc.subject	Energy Management	en_US
dc.subject	Generation Dispatch	en_US
dc.subject	Neural Networks	en_US
dc.subject	Smart Grid	en_US
dc.title	Electrical Energy Management and Load Forecasting in a Smart Grid	en_US
dc.type	Article	en_US