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An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting

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dc.citedby2
dc.contributor.authorKassim N.M.en_US
dc.contributor.authorSanthiran S.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorIslam M.A.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorMohd Yusof M.Y.en_US
dc.contributor.authorAmin N.en_US
dc.contributor.authorid57189061699en_US
dc.contributor.authorid58626157400en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57657507100en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid58625900900en_US
dc.contributor.authorid7102424614en_US
dc.date.accessioned2024-10-14T03:17:46Z
dc.date.available2024-10-14T03:17:46Z
dc.date.issued2023
dc.description.abstractThe installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm. � 2023 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo13521
dc.identifier.doi10.3390/su151813521
dc.identifier.issue18
dc.identifier.scopus2-s2.0-85172888070
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85172888070&doi=10.3390%2fsu151813521&partnerID=40&md5=305bf3d16ae9d902840fc7851653ec39
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34046
dc.identifier.volume15
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleSustainability (Switzerland)
dc.subjectdecision tree regression
dc.subjectenergy
dc.subjectforecast
dc.subjectglobal irradiance
dc.subjectlarge-scale solar PV
dc.subjectPV plant output
dc.subjectMalaysia
dc.subjectdecision analysis
dc.subjectforecasting method
dc.subjectirradiance
dc.subjectphotovoltaic system
dc.subjectpower plant
dc.subjectregression analysis
dc.subjectsolar power
dc.titleAn Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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