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Wind resource forecasting using enhanced measure correlate predict (MCP)

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dc.contributor.authorZakaria A.en_US
dc.contributor.authorFr�h W.G.en_US
dc.contributor.authorIsmail F.B.en_US
dc.contributor.authorid58061533300en_US
dc.contributor.authorid6701552426en_US
dc.contributor.authorid58027086700en_US
dc.date.accessioned2023-05-29T06:50:14Z
dc.date.available2023-05-29T06:50:14Z
dc.date.issued2018
dc.description.abstractThe enhancement of Measure Correlate Predict (MCP) using Principal Component Analysis (PCA) is a new wind prediction method based on studying the patterns of historical wind data. The method is trained based on past wind data to predict the wind speed using an ensemble of similar past events. The method is tested based on Meteorological Office (MET-Office) wind speed from a reference site that spans from 2000 to 2010. The last two years (2009 to 2010) were used as training years where the MCP - PCA algorithm learns the wind patterns between the reference(s) and target(s) site. The prediction result is then compared to the actual wind speed distribution at the target site of the training years. The method is further tested with an increase in number of reference sites for predictions. The new prediction results show that the prediction error improves to 23.1 % in average in comparison to a standard linear regression method. � 2018 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo40005
dc.identifier.doi10.1063/1.5075569
dc.identifier.scopus2-s2.0-85057320140
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057320140&doi=10.1063%2f1.5075569&partnerID=40&md5=005f9492539f6e340de61fbcf6ca720b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23557
dc.identifier.volume2035
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleWind resource forecasting using enhanced measure correlate predict (MCP)en_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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