Flower pollination-feedforward neural network for load flow forecasting in smart distribution grid

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dc.contributor.authorShehu, Gaddafi Sani.
dc.contributor.authorÇetinkaya, Nurettin.
dc.date.accessioned2020-03-26T20:14:20Z
dc.date.available2020-03-26T20:14:20Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.description.abstractNature-inspired population-based metaheuristic flower pollination algorithm is proposed in solving load flow forecasting problem in smart distribution grid environment. The efficient approach involves training a feedforward neural network (FNN) with a new flower pollination algorithm (FPA). The idea is to perform short-term load flow forecasting in smart distribution network, thus maintaining system security due to intermittency of renewable energy penetration and power flow demand. Application of optimization algorithms such as FPA in training neural network improves accuracy, overcomes generalization ability of neural network, requires less data and prevents premature convergence problem in artificial intelligence solutions due to nonlinearity of parameters. The real load flow data are collected through distribution management system of Konya Organized Industrial Zone. The result obtained indicates strong improvement in error reduction using flower pollination optimization algorithm in training FNN for short-term load flow forecasting in smart distribution grid; the model is compared against FNN model and efficient support vector regression.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThe authors acknowledge the effort of Konya Organized Industrial Zone Directorate for providing access to system data, and support of Scientific and Technological Research Council of Turkey (TUBITAK)en_US
dc.identifier.citationShehu, G. S., Çetinkaya, N. (2019). Flower Pollination–Feedforward Neural Network for Load Flow Forecasting in Smart Distribution Grid. Neural Computing and Applications, 31(10), 6001-6012.
dc.identifier.doi10.1007/s00521-018-3421-5en_US
dc.identifier.endpage6012en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue10en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage6001en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-018-3421-5
dc.identifier.urihttps://hdl.handle.net/20.500.12395/37862
dc.identifier.volume31en_US
dc.identifier.wosWOS:000491131700022en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorShehu, Gaddafi Sani.
dc.institutionauthorÇetinkaya, Nurettin.
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectFlower pollination algorithmen_US
dc.subjectFeedforward neural networken_US
dc.subjectLoad flow forecastingen_US
dc.subjectSmart distribution griden_US
dc.titleFlower pollination-feedforward neural network for load flow forecasting in smart distribution griden_US
dc.typeArticleen_US

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