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A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

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dc.citedby14
dc.contributor.authorRahman M.M.en_US
dc.contributor.authorShakeri M.en_US
dc.contributor.authorKhatun F.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorSamsudin N.A.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorHasan M.K.en_US
dc.contributor.authorid58831072700en_US
dc.contributor.authorid55433849200en_US
dc.contributor.authorid57516189300en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57190525429en_US
dc.contributor.authorid7102424614en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid55057479600en_US
dc.date.accessioned2024-10-14T03:18:29Z
dc.date.available2024-10-14T03:18:29Z
dc.date.issued2023
dc.description.abstractThe increasing energy demand and expansion of power plants are provoking the effects of greenhouse gas emissions and global warming. To mitigate these issues, renewable energies (like solar, wind, and hydropower) are blessings for modern energy sectors. The study focuses on wind-speed prediction in energy forecasting applications. This paper is a comprehensive review of deep neural network based approaches, like the �nonlinear autoregressive exogenous inputs (NARX)�, �nonlinear input-output (NIO)� and �nonlinear autoregressive (NAR)� neural network models, in time-series forecasting applications. This study proposed NARX based prediction models in wind-speed forecasting for short-term scheme. The meteorological parameters related to wind time-series have been analyzed, and used for evaluating the performance of the proposed models. The experiments revealed the best performance of the prediction models in terms of �mean square error (MSE)�, �correlation-coefficient (R2)�, �auto-correlation�, �error-histogram�, and �input-error cross-correlation�. Comparing with the other neural network models, like �recurrent neural network (RNN)� and �curve fitting neural network (CFNN)� models, the NARX-based prediction model achieved better performance in regard to �auto-correlation�, �error-histogram�, �input-error cross-correlation�, and training time. The results also showed that the RNN and CFNN models performed better prediction accuracy with R2 and MSE values. While this performance index is slightly higher, it is negligible in forecasting applications and concluded that the proposed NARX-based model achieved the better prediction accuracy in terms of other performance evaluation measures. � 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s40860-021-00166-x
dc.identifier.epage200
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85122734862
dc.identifier.spage183
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122734862&doi=10.1007%2fs40860-021-00166-x&partnerID=40&md5=b1dcf2a2de0af5ea06567d8c3bb5fafe
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34221
dc.identifier.volume9
dc.pagecount17
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleJournal of Reliable Intelligent Environments
dc.subjectNARX neural network
dc.subjectRecurrent neural network
dc.subjectRenewable energy
dc.subjectTime-series forecasting
dc.subjectWind-speed prediction
dc.subjectCurve fitting
dc.subjectDeep neural networks
dc.subjectErrors
dc.subjectGas emissions
dc.subjectGlobal warming
dc.subjectGreenhouse gases
dc.subjectMean square error
dc.subjectRecurrent neural networks
dc.subjectTime series
dc.subjectTime series analysis
dc.subjectWeather forecasting
dc.subjectWind speed
dc.subjectForecasting: applications
dc.subjectNARX neural network
dc.subjectNetwork-based approach
dc.subjectNeural network model
dc.subjectPerformance
dc.subjectPrediction modelling
dc.subjectRenewable energies
dc.subjectTime series forecasting
dc.subjectWind speed prediction
dc.subjectWind time series
dc.subjectNeural network models
dc.titleA comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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