Single- and combined-source typical metrological year solar energy data modelling

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dc.authorscopusid57057224800en_US
dc.authorscopusid57191913729en_US
dc.authorscopusid57190847465en_US
dc.authorscopusid57202959651en_US
dc.authorscopusid57207967011en_US
dc.authorscopusid57203629684en_US
dc.authorscopusid56571673200en_US
dc.contributor.authorAfzal, Asif
dc.contributor.authorBuradi, Abdulrajak
dc.contributor.authorAlwetaishi, Mamdooh
dc.contributor.authorAgbulut, Umit
dc.contributor.authorKim, Boyoung
dc.contributor.authorKim, Hyun-Goo
dc.contributor.authorPark, Sung Goon
dc.date.accessioned2024-08-23T16:07:07Z
dc.date.available2024-08-23T16:07:07Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractPrediction of solar energy data is very crucial for the effective utilization of freely available renewable energy abundantly in nature. Solar energy data are widely available which must be carefully prepared and arranged for modelling. In this work, typical meteorological year (TMY) data made available by the Korea institute of energy research (KIER) and the National renewable energy laboratory (NREL) are used for modelling in different phases. TMY data at single-point location and multiple locations from KIER are initially used for training of machine learning (ML) algorithms. Later, the TMY data from NREL and KIER are combined and then modelled using radius nearest neighbour (RNN), decision tree regressor (DTR), random forest regressor (RFR), and X-gradient boosting (XGB) algorithms. The solar energy parameters modelled in this work are dew point temperature (DPT), dry bulb temperature (DBT), relative humidity (RH), surface pressure (SP), windspeed (WS), and solar insolation of horizontal plane (IHP). Quantitative analysis of the algorithms is also performed in each stage of the work. The modelling indicates that the DBT, DPT, RH, and SP are able to be predicted with a minimum accuracy of over 90% in each stage. The WS and IHP data when modelled from a single-source TMY data provide superior accuracy than when they are combined. RFR and XGB have outperformed overall as they provide good accuracy for WS and IHP data as well. RNN and DTR achieved 100% accuracy in training, while RFR and XGB showed slightly lower training accuracy due to their avoidance of overfitting. There are errors in testing for RNN/DTR. Using RNN/DTR, the training errors are 0% in all cases, while in some cases like DTP the error by RFR/XGB up to 3%, whereas RNN/DTR testing errors go up to 5% and in case of RFR/XGB they are up to 7.5%. For RH modelling RFR/XGB, training errors are max 6%. RNN/DTR testing errors go up to 11%, while for RFR/XGB up to 7.5% which indicates their robustness. It is observed that many solar parameters, when combined with different source data, can be predicted easily with good accuracy, while WS and IHP become a little bit challenging to model.en_US
dc.description.sponsorshipNational research foundation of Koreaen_US
dc.description.sponsorshipThis study was conducted with the support of the National Research Foundation of Korea (NRF- 2021R1C1C1008791).en_US
dc.identifier.doi10.1007/s10973-023-12604-4
dc.identifier.endpage12523en_US
dc.identifier.issn1388-6150
dc.identifier.issn1588-2926
dc.identifier.issue22en_US
dc.identifier.scopus2-s2.0-85176800715en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage12501en_US
dc.identifier.urihttps://doi.org/10.1007/s10973-023-12604-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14503
dc.identifier.volume148en_US
dc.identifier.wosWOS:001205211800015en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Thermal Analysis and Calorimetryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSolar energy modellingen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectTypical meteorological yearen_US
dc.subjectRenewable energy predictionen_US
dc.subjectSolar parameter forecastingen_US
dc.subjectEnergy data integrationen_US
dc.subjectRadiation Predictionen_US
dc.subjectParameter Extractionen_US
dc.subjectMachineen_US
dc.subjectSvmen_US
dc.titleSingle- and combined-source typical metrological year solar energy data modellingen_US
dc.typeArticleen_US

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