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Hybrid metaheuristic of artificial neural network - Bat algorithm in forecasting electricity production and water consumption at Sultan Azlan shah Hydropower plant

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dc.citedby3
dc.contributor.authorHussin S.N.H.S.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorJaddi N.S.en_US
dc.contributor.authorHamid Z.A.en_US
dc.contributor.authorid57194948280en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid36716354300en_US
dc.contributor.authorid52663349600en_US
dc.date.accessioned2023-05-29T06:38:14Z
dc.date.available2023-05-29T06:38:14Z
dc.date.issued2017
dc.descriptionHydroelectric power; Hydroelectric power plants; Neural networks; Bat algorithms; Bio-inspired algorithms; Electricity production; Forecasting electricity; Hybrid Meta-heuristic; Hydropower; Renewable energies; Water consumption; Electric power generationen_US
dc.description.abstractHydropower is one of the technologies in renewable energy that is commercially viable on a large scale. A hybrid of metaheuristic Artificial Neural Network (ANN) technique with Bat Algorithm (BA), a bio-inspired algorithm is proposed to forecast future electricity production and water consumption at Sultan Azlan Shah Hydropower Dam located upstream of Perak river. In this study, both the ANN and Hybrid ANN-Bat Algorithm coding was designed and written explicitly to tailor the time series input data and assumptions used in this study. Comparison on results obtained from ANN and the proposed hybrid ANN - BA was conducted. Simulations conducted in this study exhibited that the proposed hybrid algorithm is much superior then the conventional ANN. � 2016 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7951467
dc.identifier.doi10.1109/PECON.2016.7951467
dc.identifier.epage31
dc.identifier.scopus2-s2.0-85024392000
dc.identifier.spage28
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85024392000&doi=10.1109%2fPECON.2016.7951467&partnerID=40&md5=3d5b121abf688f88105a76326c32de1e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23182
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitlePECON 2016 - 2016 IEEE 6th International Conference on Power and Energy, Conference Proceeding
dc.titleHybrid metaheuristic of artificial neural network - Bat algorithm in forecasting electricity production and water consumption at Sultan Azlan shah Hydropower planten_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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