Short Term Load Forecasting Using Artificial Neural Networks

dc.contributor.author	Gichovi, Ireri T.
dc.contributor.author	Murage, Daniel K.
dc.contributor.author	Abungu, Nicodemus
dc.date.accessioned	2019-07-30T07:25:36Z
dc.date.available	2019-07-30T07:25:36Z
dc.date.issued	2013
dc.identifier.issn	2079-6226
dc.identifier.uri	http://ir.mksu.ac.ke/handle/123456780/4670
dc.description.abstract	Load forecasting refers to the prediction of future load conditions based on present or historical data. This is important especially for transmission planning and economic dispatch. In this paper, an Artificial Neural Network (ANN) is trained using historical data for a sub-station at Ruiru, Kenya and the corresponding loading conditions for the sub-station are used to test its accuracy in forecasting the electrical load when given other parameters.	en_US
dc.language.iso	en_US	en_US
dc.publisher	Proceedings of 2013 Mechanical Engineering Conference on Sustainable Research and Innovation	en_US
dc.subject	ANN	en_US
dc.subject	Load Forecasting	en_US
dc.title	Short Term Load Forecasting Using Artificial Neural Networks	en_US
dc.type	Article	en_US

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