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master's thesis
Kratkoročno predviđanje potrošnje električne energije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. urn:nbn:hr:168:784624
Deak, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Deak, M. (2015). Kratkoročno predviđanje potrošnje električne energije (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:784624

Deak, Marko. "Kratkoročno predviđanje potrošnje električne energije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:784624

Deak, Marko. "Kratkoročno predviđanje potrošnje električne energije." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2015. https://urn.nsk.hr/urn:nbn:hr:168:784624

Deak, M. (2015). 'Kratkoročno predviđanje potrošnje električne energije', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 10 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:784624

Deak M. Kratkoročno predviđanje potrošnje električne energije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2015 [cited 2024 May 10] Available at: https://urn.nsk.hr/urn:nbn:hr:168:784624

M. Deak, "Kratkoročno predviđanje potrošnje električne energije", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2015. Available at: https://urn.nsk.hr/urn:nbn:hr:168:784624

Metadata
TitleKratkoročno predviđanje potrošnje električne energije
Title (english)Short term load forecasting
AuthorMarko Deak
MentorDomagoj Jakobović (mentor)
Committee memberDomagoj Jakobović (predsjednik povjerenstva)
Committee memberMarko Čupić (član povjerenstva)
Committee memberLeonardo Jelenković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2015-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Problem kratkoročnog predviđanja potrošnje električne energije jedan je od osnovnih primjera prognoze vrijednosti vremenskih sljedova te ima značajnu ulogu u električnoj opskrbi. Svrha ovog rada je demonstrirati primjenu metoda genetskog programiranja na problemu kratkoročnog predviđanja potrošnje električne energije te usporedba rezultata sa postojećim dobivenim primjenom drugih metoda. Uz osnovnu implementaciju genetskog programiranja dodano je i linearno skaliranje te semantičko genetsko programiranje za poboljšanje dobivenog rješenja te strože poštivanje semantike prisutne u ulaznim podatcima. Najveći dio optimizacije dobivenih modela implementacija je metoda skupnog učenja primijenjenih na genetsko programiranje, odnosno algoritama GPBoost i BCC. Prikazani su rezultati implementiranog algoritma te njegovih različitih varijacija uz utjecaj promjene parametara algoritma na kvalitetu najboljeg dobivenog rješenja. Konačno su dana razmatranja rezultata te kraće ideje za daljnja poboljšanja rezultata.
Abstract (english)
The short-term electric energy load forecasting problem is one of the basic examples of time series forecasting and has an important role in electric power distribution. The goal of this paper is to demonstrate the application of genetic programming methods on the problem of short-term load forecasting and to compare results with the existing ones gained when using other methods. Together with the basic implementation of genetic programming linear scaling and semantic genetic programming was added to improve the current solution and enforce the stricter following of the semantics which exist in the input data. The largest part of optimization of current models was the implementation of ensemble learning methods applied to genetic programming, namely algorithms GPBoost and BCC. Results of the implemented algorithm and its different variations are presented together with the effect that changing of algorithm parameters has on the quality of the best solution. Finally, further consideration of results is given as well as short ideas for further improvement of the results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:784624
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2018-02-06 08:24:52