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dc.contributor.advisorStajić, Zoran
dc.contributor.otherStanković, Milena
dc.contributor.otherRajaković, Nikola
dc.contributor.otherKorunović, Lidija
dc.contributor.otherJanić, Aleksandar
dc.creatorBožić, Miloš M.
dc.date.accessioned2017-09-19T10:28:07Z
dc.date.available2017-09-19T10:28:07Z
dc.date.available2020-07-03T16:02:23Z
dc.date.issued2015-03-30
dc.identifier.urihttp://nardus.mpn.gov.rs/handle/123456789/8577
dc.identifier.urihttp://eteze.ni.ac.rs/application/showtheses?thesesId=5257
dc.identifier.urihttps://fedorani.ni.ac.rs/fedora/get/o:1397/bdef:Content/download
dc.identifier.urihttp://vbs.rs/scripts/cobiss?command=DISPLAY&base=70052&RID=533687190
dc.description.abstractThe topic of this dissertation is a short-term load forecasting using artificial intelligence methods. Three new models with least squares support vector machines for nonlinear regression are proposed. First proposed model is a model with forecasting in two stages. This model use additioal feature, maximum daily load which is not known for day ahead. Forecating of maximum daily load is obtained in the first stage. This forecasted value is used in second stage, where forecasting of hourly load is done. Model with feature selection, using mutual information for selection criteria, is a second proposed model. This model tries to find an optimal feature set for a given problem. Forecasting model based on an incremental update scheme is a third proposed model. This model is based on the incremental update of the initial training set by adding new instances into it as soon as they become available and throwing out the old ones. Then the model is trained with new training set. By this approach the evolving nature of the load pattern is followed and the model performance is preserved and improved. For models evaluation, the forecasting of hourly loads for one year is done. Electrical consumption data for the City of Niš, which have about 260000 habitans and average daily demand of 182 MW, is used for testing. Double sesonal ARIMA and Holt-Winters as representatives of clasical models and artificial neural networks, least squares support vector machines and relevance vector machines as representatives of artificial models, are used for models evaluation. For a measure of accuracy, mean absolute percentage error, symetrical mean absolute percentage error, square root mean error and absolute percentage error are used. Obtained results show that the best model is model with incremental update scheme, followed by double sesonal ARIMA and artificial neural networks models. The worst results are obtained by relevance vector machines and double sesonal Holt-Winters models. It has been shown that the best model could be successfully used with the short-term load forecasting problem.en
dc.formatapplication/pdf
dc.languagesr
dc.publisherУниверзитет у Нишу, Електронски факултетsr
dc.rightsopenAccessen
dc.sourceУниверзитет у Нишуsr
dc.subjectkratkoroĉna prognoza potrošnje elektriĉne energijesr
dc.subjectshort-term electrical load forecastingen
dc.subjectartificial intelligenceen
dc.subjectmachine learningen
dc.subjectelectrical loaden
dc.subjecttime seriesen
dc.subjectveštaĉka inteligencijasr
dc.subjectmašinsko uĉenjesr
dc.subjectelektriĉno opterećenjesr
dc.subjectvremenske serijesr
dc.titleKratkoročna prognoza potrošnje električne energije zasnovana na metodama veštačke inteligencijesr
dc.typedoctoralThesis
dc.rights.licenseBY-NC-SA
dcterms.abstractСтајић, Зоран; Станковић, Милена; Рајаковић, Никола; Коруновић, Лидија; Јанић, Aлександар; Божић, Милош М.; Краткорочна прогноза потрошње електричне енергије заснована на методама вештачке интелигенције; Краткорочна прогноза потрошње електричне енергије заснована на методама вештачке интелигенције;
dc.identifier.fulltexthttp://nardus.mpn.gov.rs/bitstream/id/52230/Disertacija.pdf


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