Intelligent Energy Management and Prediction of Micro Grid Operation Based On Machine Learning and Genetic Algorithm

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dc.authoridGUNESER, Muhammet Tahir/0000-0003-3502-2034
dc.contributor.authorElweddad, Mohamed
dc.contributor.authorGuneser, Muhammet Tahir
dc.date.accessioned2024-09-29T16:12:20Z
dc.date.available2024-09-29T16:12:20Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMicro grid energy management has become critically important due to inefficient power use in the residential sector. High energy consumption necessitates developing a strategy to manage the power flow efficiently. For this purpose, this work has been divided into two phases: The first is the ON/OFF operation, which has been executed using a genetic algorithm for the hybrid system, including diesel generator, solar photovoltaic (PV), wind turbine, and battery. Then, in the second phase, the output results were used as input in three algorithms to predict load and supply dispatch one month ahead. This study has two objectives; the first is to decide which energy source should meet the load one month ahead. The second is to compare the outcomes of machine-learning techniques, namely Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN), to determine the one that performs the best. The results indicated that the DT technique has the best performance in the application of classification with an accuracy of 100%. The findings also show that the RF approach gives acceptable results with an accuracy of up to 98%, and the KNN algorithm was poor in terms of accuracy with a value of 28%.en_US
dc.identifier.endpage2014en_US
dc.identifier.issn1309-0127
dc.identifier.issue4en_US
dc.identifier.startpage2002en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14619/8703
dc.identifier.volume12en_US
dc.identifier.wosWOS:000905144200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherInt Journal Renewable Energy Researchen_US
dc.relation.ispartofInternational Journal of Renewable Energy Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRenewable energyen_US
dc.subjectPower managementen_US
dc.subjectLoad classificationen_US
dc.subjectMachine learning algorithmsen_US
dc.titleIntelligent Energy Management and Prediction of Micro Grid Operation Based On Machine Learning and Genetic Algorithmen_US
dc.typeArticleen_US

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