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An Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysia

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dc.contributor.authorHwang Y.K.en_US
dc.contributor.authorIbrahim M.Z.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAlbani A.en_US
dc.contributor.authorid57207781142en_US
dc.contributor.authorid55413616900en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55772882600en_US
dc.date.accessioned2023-05-29T07:27:03Z
dc.date.available2023-05-29T07:27:03Z
dc.date.issued2019
dc.descriptionData mining; Genetic algorithms; Meteorology; Neural networks; Planning; Sustainable development; Weibull distribution; Climate forecasts; Measure-correlate-predict; Measurement instruments; Measurement sites; Meteorological data; Reanalysis; Weibull frequency; Wind measurement; Forecastingen_US
dc.description.abstractThe major issues on the wind measurement campaign are the data measured in a short period and the occurrence of missing data due to the failure of the measurement instrument. Meanwhile, Measure-Correlate-Predict (MCP) method had widely been used to predict the long-term condition and missing data at the measurement site based on nearest Malaysian Meteorological Department (MMD), Meteorological Aerodrome Report (METAR) and extended Climate Forecast System Reanalysis (ECFSR) data. In this research, the long-term wind data at selected potential sites in Malaysia were predicted by optimized Artificial Neural Networks (ANNs). The Genetic Algorithm (GA) was applied to optimize the ANN. Five different ANN MCP models had been designed based on different types of reference data and different temporal scales to predict wind data at three target sites. Weibull frequency distributions and RMSE examined predicted wind data. The prediction of ANN had been improved in between 20.562% to 113.573% by GA optimization. The best R-value obtained from optimization were affected the Weibull shape and scale of predicted data. At last, the result revealed that the optimized ANN model could predict the long-term data for the target site with better accuracy. � 2018 Asian Institute of Technology.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8635790
dc.identifier.doi10.23919/ICUE-GESD.2018.8635790
dc.identifier.scopus2-s2.0-85062879361
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062879361&doi=10.23919%2fICUE-GESD.2018.8635790&partnerID=40&md5=8169245adf0af7ac042248f7c1613c9d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24787
dc.identifier.volume2018-October
dc.publisherIEEE Computer Societyen_US
dc.sourceScopus
dc.sourcetitleProceedings of the Conference on the Industrial and Commercial Use of Energy, ICUE
dc.titleAn Optimized ANN Measure-Correlate-Predict Method for Long-term Wind Prediction in Malaysiaen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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