Publication:
Application of Extreme Learning Machine in Predicting Short- Term Wind Speed

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dc.contributor.authorChen C.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorNasser A.A.en_US
dc.contributor.authorAbbas D.en_US
dc.contributor.authorChooi F.Y.en_US
dc.contributor.authorid25824552100en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid57220806149en_US
dc.contributor.authorid57218304981en_US
dc.contributor.authorid57220806223en_US
dc.date.accessioned2023-05-29T08:08:00Z
dc.date.available2023-05-29T08:08:00Z
dc.date.issued2020
dc.descriptionElectric load dispatching; Electric power transmission networks; Forecasting; Knowledge acquisition; Machine learning; Mean square error; Speed; Wind power; Developed model; Economic advantages; Extreme learning machine; Prediction accuracy; Root mean square errors; Scientific basis; Strong stability; Sustainable resources; Winden_US
dc.description.abstractAt present, wind energy is the fastest growing power generation sector with its economic advantage of being a rich, clean and environmentally sustainable resources. However, wind does not generally blow consistently which prevents wind turbines from functioning at maximum capacity and capability. However, the solution that had been put forward in this paper to overcome the aforementioned problem is able to be used to make the deterministic predictions study for the wind speed, and of which its model in this paper possesses a significant prediction accuracy and strong stability, which could be useful in predicting the randomness of short term wind speed accurately. Based on the prediction output results, the amount of power required to be generated for load dispatch planning could be calculated and used to produce a scientific basis with the purpose of designing an optimal power grid dispatching design. In this paper, Extreme Learning Machine (ELM) is used for predicting short-term wind speed, and through use of the ELM the prediction accuracy of wind speed was observed to be at 0.93 followed by the root mean square error rate at 1.9. With reference to the prediction results, the developed model is tested to be able to predict wind speed accurately. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243315
dc.identifier.doi10.1109/ICIMU49871.2020.9243315
dc.identifier.epage199
dc.identifier.scopus2-s2.0-85097652617
dc.identifier.spage194
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097652617&doi=10.1109%2fICIMU49871.2020.9243315&partnerID=40&md5=6e5f1e2e30f74b8a7611ccea1e112ae6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25304
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleApplication of Extreme Learning Machine in Predicting Short- Term Wind Speeden_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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