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Wind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysia

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dc.contributor.authorKim Hwang Y.en_US
dc.contributor.authorZamri Ibrahim M.en_US
dc.contributor.authorIsmail M.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorAlbani A.en_US
dc.contributor.authorid57317168600en_US
dc.contributor.authorid57207778474en_US
dc.contributor.authorid57210403363en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55772882600en_US
dc.date.accessioned2023-05-29T09:37:26Z
dc.date.available2023-05-29T09:37:26Z
dc.date.issued2022
dc.descriptionComplex networks; Forecasting; Genetic algorithms; Neural networks; Wind; Wind power; Case-studies; Complex terrains; Malaysia; Malaysians; MCP; Measure-correlate-predict; Model inputs; Spatial modelling; Wind maps; Wind speed models; Surface roughness; alternative energy; artificial neural network; genetic algorithm; renewable resource; roughness; wind power; Malaysiaen_US
dc.description.abstractThis study aimed to create a Malaysian wind map of greater accuracy. Compared to a previous wind map, spatial modeling input was increased. The Genetic Algorithm-optimized Artificial Neural Network Measure�Correlate�Predict method was used to impute missing data, and managed to control over- or under-prediction issues. The established wind map was made more reliable by including surface roughness to simulate wind flow over complex terrain. Validation revealed that the current wind map is 33.833% more accurate than the previous wind map. Furthermore, the correlation coefficient between wind map-simulated data and observed data was high as 0.835. In conclusion, the new and improved wind map for Malaysia simulates data with acceptable accuracy. � The Author(s) 2021.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1177/0309524X211055836
dc.identifier.epage843
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85118243117
dc.identifier.spage818
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85118243117&doi=10.1177%2f0309524X211055836&partnerID=40&md5=d111dee6eeb90f8f57d3c29cc9c29e44
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26873
dc.identifier.volume46
dc.publisherSAGE Publications Inc.en_US
dc.sourceScopus
dc.sourcetitleWind Engineering
dc.titleWind speed modeling over complex terrain with the artificial neural network in the measure-correlate-predict technique: A case study of Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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