Publication:
Cascade hydropower discharge flow prediction based on dynamic artificial neural networks

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dc.citedby1
dc.contributor.authorAnuar N.N.en_US
dc.contributor.authorKhan M.R.B.en_US
dc.contributor.authorRamli A.F.en_US
dc.contributor.authorJidin R.en_US
dc.contributor.authorOthman A.B.en_US
dc.contributor.authorid57225903949en_US
dc.contributor.authorid55812128900en_US
dc.contributor.authorid57192170265en_US
dc.contributor.authorid6508169028en_US
dc.contributor.authorid49561714800en_US
dc.date.accessioned2023-05-29T09:11:28Z
dc.date.available2023-05-29T09:11:28Z
dc.date.issued2021
dc.description.abstractRainy seasons with heavy rainfall in catchment zones cause high potential of flooding at downstream, primarily due to the reservoirs' capacity limit been surpassed. Discharge flow prediction can be used for the hydropower plant to limit downstream flow during rainy seasons. In this study, discharge flow prediction based on the Artificial Neural Network (ANN) is proposed in order to forecast hydropower discharges flow. A cascade hydropower scheme has been selected for this study. Data such as fore-bay elevation, inflow, and discharge flow from the cascade hydropower power plants have been collected and used as an input for the ANN models. The developed models are Feedforward Backpropagation Neural Network, Elman Neural Network, and Nonlinear Autoregressive with Exogenous Inputs (NARX). The models have been assessed with different training methods and the number of hidden neurons to assess their performances. Moreover, the models' flow prediction performances been compared to the conventional Water Balance methodology. The result shows Elman Neural Network demonstrates higher prediction accuracy compared to other techniques based on the statistical error measures. � School of Engineering, Taylor's University.en_US
dc.description.natureFinalen_US
dc.identifier.epage2099
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85109613492
dc.identifier.spage2080
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85109613492&partnerID=40&md5=e5245490e4a9bbd2310e8476ce0dc4bd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26522
dc.identifier.volume16
dc.publisherTaylor's Universityen_US
dc.sourceScopus
dc.sourcetitleJournal of Engineering Science and Technology
dc.titleCascade hydropower discharge flow prediction based on dynamic artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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