Data mining and analysis for predicting electrical energy consumption

Basit Öğe Kaydı
dc.contributor.authorAlmohammed, Mustafa
dc.contributor.authorFarhan, Hameed Mutlag
dc.contributor.authorNaseri, Raghad Awad Sababan
dc.contributor.authorTürkben, Ayça Kurnaz
dc.date.accessioned2023-06-10T09:43:49Z
dc.date.available2023-06-10T09:43:49Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractIn this research, data mining techniques that included the deep learning with different scenario is presented for predicting electrical energy consumption data. Energy of the features are implemented and calculated from the electrical energy consumption data. Different scenario with neuron numbers for artificial neural network are investigated and analyzed. Also the results are compared with other k-fold numbers of the data. The results of proposed method shows that the proposed methods has high accuracy and high performance.en_US
dc.identifier.citationAlmohammed, M., Farhan, H. M., Naseri, R. A. S., & Turkben, A. K. (2022). Data mining and analysis for predicting electrical energy consumption. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT). IEEE.en_US
dc.identifier.scopus2-s2.0-85160536188
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3543
dc.indekslendigikaynakScopus
dc.institutionauthorAlmohammed, Mustafa
dc.institutionauthorFarhan, Hameed Mutlag
dc.institutionauthorNaseri, Raghad Awad Sababan
dc.institutionauthorTürkben, Ayça Kurnaz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022
dc.relation.isversionof10.1109/ICAIoT57170.2022.10121820en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectData Mining Techniquesen_US
dc.subjectElectrical Energy Consumption Dataen_US
dc.subjectFeature Analysisen_US
dc.subjectPredictingen_US
dc.titleData mining and analysis for predicting electrical energy consumption
dc.typeConference Object

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