Publication:
Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks

Simple item page
dc.citedby31
dc.contributor.authorRahman M.M.en_US
dc.contributor.authorShakeri M.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKhatun F.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.authorid15128307800en_US
dc.contributor.authorid57516189300en_US
dc.contributor.authorid7102424614en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid55057479600en_US
dc.date.accessioned2023-05-29T09:09:07Z
dc.date.available2023-05-29T09:09:07Z
dc.date.issued2021
dc.descriptionalternative energy; artificial neural network; complexity; demand analysis; machine learning; prediction; rural area; smart grid; time series analysisen_US
dc.description.abstractThis paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world�s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A �hybrid-renewable-energy system� (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as �multi-layer perception� (MLP), �recurrent-neural network� (RNN), and �convolutional-neural network� (CNN), as well as �long-short-term memory� (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2393
dc.identifier.doi10.3390/su13042393
dc.identifier.epage28
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85102174204
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85102174204&doi=10.3390%2fsu13042393&partnerID=40&md5=aa6b7f8a4cb343a343462a5f9de0aab1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26326
dc.identifier.volume13
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.titleProspective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networksen_US
dc.typeReviewen_US
dspace.entity.typePublication
Files
Collections
SCOPUS