Short-term fuzzy load forecasting model using genetic-fuzzy and ant colony-fuzzy knowledge base optimization

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dc.contributor.authorLuy M.
dc.contributor.authorAtes V.
dc.contributor.authorBarisci N.
dc.contributor.authorPolat H.
dc.contributor.authorCam E.
dc.date.accessioned2020-06-25T15:17:58Z
dc.date.available2020-06-25T15:17:58Z
dc.date.issued2018
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThe estimation of hourly electricity load consumption is highly important for planning short-term supply-demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base-which is defined by expert insights and decisions-gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA) techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE) of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014). The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment. © 2018 by the authors.en_US
dc.description.sponsorship2016/127en_US
dc.description.sponsorshipAcknowledgments: This research was supported by Scientific Research Projects Coordination Unit (B.A.P.K.B.) of Kırıkkale University (Project No: 2016/127, 2017).en_US
dc.identifier.doi10.3390/app8060864
dc.identifier.issn20763417
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85047529392
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/app8060864
dc.identifier.urihttps://hdl.handle.net/20.500.12587/2582
dc.identifier.volume8en_US
dc.identifier.wosWOS:000436488000023
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI AGen_US
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnt colony optimizationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectFuzzy logicen_US
dc.subjectGenetic algorithmen_US
dc.subjectShort-term load forecastingen_US
dc.titleShort-term fuzzy load forecasting model using genetic-fuzzy and ant colony-fuzzy knowledge base optimizationen_US
dc.typeArticle

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