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Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

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dc.contributor.authorMd Azmi C.S.A.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorHen C.K.en_US
dc.contributor.authorNoman F.en_US
dc.contributor.authorPaw J.K.S.en_US
dc.contributor.authorTak Y.C.en_US
dc.contributor.authorAlshetwi A.Q.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorid57560236000en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid36994481200en_US
dc.contributor.authorid55327881300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid57560453900en_US
dc.contributor.authorid57559574500en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid57216824752en_US
dc.date.accessioned2023-05-29T09:38:01Z
dc.date.available2023-05-29T09:38:01Z
dc.date.issued2022
dc.description.abstractWind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R�) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection. � 2022 NSP Natural Sciences Publishing Cor.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18576/isl/110217
dc.identifier.epage473
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85127466281
dc.identifier.spage465
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127466281&doi=10.18576%2fisl%2f110217&partnerID=40&md5=f1c8eaaa17afced39f8b4def26d996dd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26942
dc.identifier.volume11
dc.publisherNatural Sciences Publishingen_US
dc.sourceScopus
dc.sourcetitleInformation Sciences Letters
dc.titleUnivariate and Multivariate Regression Models for Short-Term Wind Energy Forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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