Importance of Holidays for Short Term Load Forecasting Using Adaptive Neural Fuzzy Inference System

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dc.contributor.authorAkdemir, Bayram
dc.contributor.authorCetinkaya, Nurettin
dc.date.accessioned2020-03-26T18:30:42Z
dc.date.available2020-03-26T18:30:42Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Conference on Materials Science and Information Technology (MSIT 2011) -- SEP 16-18, 2011 -- Singapore, SINGAPOREen_US
dc.description.abstractIn distributing systems, load forecasting is one of the major management problems to carry on energy flowing; protect the systems, and economic management. In order to manage the system, next step of the load characteristics must be inform from historical data sets. For the forecasting, not only historical parameters are used but also external parameters such as weather conditions, seasons and populations and etc. have much importance to forecast the next behavior of the load characteristic. Holidays and week days have different affects on energy consumption in any country. In this study, target is to forecast the peak energy level the next an hour and to compare affects of week days and holidays on peak energy needs. Energy consumption data sets have nonlinear characteristics and it is not easy to fit any curve due to its nonlinearity and lots of parameters. In order to forecast peak energy level, Adaptive neural fuzzy inference system is used for hourly affects of holidays and week days on peak energy level is argued. The obtained values from output of the artificial intelligence are evaluated two fold cross validation and mean absolute percentage error. The obtained two fold cross validation error as mean absolute percentage error is 3.51 and included holidays data set has more accuracy than the data set without holiday. Total success increased 2.4%.en_US
dc.description.sponsorshipSingapore Inst Electen_US
dc.identifier.citationAkdemir, B., Cetinkaya, N., (2012). Importance of Holidays for Short Term Load Forecasting Using Adaptive Neural Fuzzy Inference System. Materials Science and Information Technology. (443-444), 3959-3963. doi:10.4028.
dc.identifier.doi10.4028/www.scientific.net/AMR.433-440.3959en_US
dc.identifier.endpage3963en_US
dc.identifier.isbn978-3-03785-319-1
dc.identifier.issn1022-6680en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage3959en_US
dc.identifier.urihttps://dx.doi.org/10.4028/www.scientific.net/AMR.433-440.3959
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28129
dc.identifier.volume433-440en_US
dc.identifier.wosWOS:000302092001186en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAkdemir, Bayram
dc.institutionauthorCetinkaya, Nurettin
dc.language.isoenen_US
dc.publisherTrans Tech Publications Ltden_US
dc.relation.ispartofMaterials Science and Information Technology, Pts 1-8en_US
dc.relation.ispartofseriesAdvanced Materials Research
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAdaptive neural fuzzy inference systemen_US
dc.subjectshort-term forecastingen_US
dc.subjectenergyen_US
dc.subjecthourlyen_US
dc.subjectholidayen_US
dc.titleImportance of Holidays for Short Term Load Forecasting Using Adaptive Neural Fuzzy Inference Systemen_US
dc.typeConference Objecten_US

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