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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.citedby4
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.authorid57207730841en_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.accessioned2023-05-29T09:41:59Z
dc.date.available2023-05-29T09:41:59Z
dc.date.issued2022
dc.descriptionCurve fitting; Deep neural networks; Errors; Forecasting; Gas emissions; Gas plants; Global warming; Graphic methods; Mean square error; Recurrent neural networks; Time series; Time series analysis; Wind; Forecasting: applications; NARX neural network; Network-based approach; Neural network model; Performance; Prediction modelling; Renewable energies; Time series forecasting; Wind speed prediction; Wind time series; Greenhouse gasesen_US
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.natureArticle in Pressen_US
dc.identifier.doi10.1007/s40860-021-00166-x
dc.identifier.scopus2-s2.0-85122734862
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/123456789/27274
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleJournal of Reliable Intelligent Environments
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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