Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/2053
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dc.contributor.authorFadare, D. A.-
dc.contributor.authorDahunsi, O. A.-
dc.date.accessioned2018-10-11T11:00:39Z-
dc.date.available2018-10-11T11:00:39Z-
dc.date.issued2009-11-
dc.identifier.issn1551-7624-
dc.identifier.otherui_art_fadare_modeling_2009-
dc.identifier.otherThe Pacific Journal of Science and Technology 10(2), pp. 471-478-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/2053-
dc.description.abstractIn this study, the short-term load pattern for the University of Ibadan was investigated and a multi-layered feed-forward artificial neural networks (ANN) model was developed to forecast the time series half-hourly load pattern of the system using the load data for a period of 5 years (2000 to 2004). The study showed that the mean half-hourly load for the period of study ranged between 1.3 and 2.2 MW, and the coefficient of determination (R2-values) of the ANN predicted and the measured half-hourly load for test dataset decreased from 0.6832 to 0.4835 with increase in the lead time from 0.5 to 10.0 hours.en_US
dc.language.isoenen_US
dc.publisherAkamai Universityen_US
dc.titleModeling and forecasting of short-term half-hourly electric load at the University of Ibadan, Nigeriaen_US
dc.typeArticleen_US
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