Publication:
Investigating photovoltaic solar power output forecasting using machine learning algorithms

Simple item page
dc.citedby7
dc.contributor.authorEssam Y.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorRamli R.en_US
dc.contributor.authorChau K.-W.en_US
dc.contributor.authorIdris Ibrahim M.S.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorSefelnasr A.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57203146903en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid56212747600en_US
dc.contributor.authorid7202674661en_US
dc.contributor.authorid57735870800en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid6505592467en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:39:34Z
dc.date.available2023-05-29T09:39:34Z
dc.date.issued2022
dc.description.abstractSolar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States� National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R 2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting. � 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1080/19942060.2022.2126528
dc.identifier.epage2034
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85139112710
dc.identifier.spage2002
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85139112710&doi=10.1080%2f19942060.2022.2126528&partnerID=40&md5=c286adff731a5ea10e38429a85941579
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27102
dc.identifier.volume16
dc.publisherTaylor and Francis Ltd.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEngineering Applications of Computational Fluid Mechanics
dc.titleInvestigating photovoltaic solar power output forecasting using machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS