prikaz prve stranice dokumenta Primjena metode potpornih vektora za dugoročno predviđanje potrošnje
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master's thesis
Primjena metode potpornih vektora za dugoročno predviđanje potrošnje
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. urn:nbn:hr:168:523601
Brna, Barbara
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Brna, B. (2014). Primjena metode potpornih vektora za dugoročno predviđanje potrošnje (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:523601

Brna, Barbara. "Primjena metode potpornih vektora za dugoročno predviđanje potrošnje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:523601

Brna, Barbara. "Primjena metode potpornih vektora za dugoročno predviđanje potrošnje." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2014. https://urn.nsk.hr/urn:nbn:hr:168:523601

Brna, B. (2014). 'Primjena metode potpornih vektora za dugoročno predviđanje potrošnje', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:523601

Brna B. Primjena metode potpornih vektora za dugoročno predviđanje potrošnje [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2014 [cited 2024 June 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:523601

B. Brna, "Primjena metode potpornih vektora za dugoročno predviđanje potrošnje", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2014. Available at: https://urn.nsk.hr/urn:nbn:hr:168:523601

Metadata
TitlePrimjena metode potpornih vektora za dugoročno predviđanje potrošnje
Title (english)Long term load forecasting based on support vector machine
AuthorBarbara Brna
MentorIgor Kuzle (mentor)
Committee memberIgor Kuzle (predsjednik povjerenstva)
Committee memberTomislav Tomiša (član povjerenstva)
Committee memberVladimir Mikuličić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2014-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Dugoročno predviđanje potrošnje električne energije ima važnu ulogu prilikom planiranja izgradnje i obnavljanja infrastrukture. Sa napretkom tehnologije, umjetna inteligencija ima sve veću ulogu u predviđanju. Uz genetske algoritme, neuronske mreže, "fuzzy" logiku, "wavelet" mreže i ekspertne sustave, najvažnija metoda je metoda potpornih vektora. Ta metoda koristi strojno učenje za razvijanje modela kojim tada radi predviđanje. U ovom radu metoda potpornih vektora korištena je za dugoročno predviđanje potrošnje Krapinsko-zagorske županije. Faktori kojima je razvijen model su bili potrošnja u prošlosti, te BDP Krapinsko-zagorske županije. Prikazani su rezultati predviđanja dobiveni različitim metodama provjere, te je odabrano predviđanje koje daje najvjerojatnije rezultate.
Abstract (english)
Long term load forecasting has a very important role in developing and reconstruction of infrastructure. Because of technology advances, artificial intelligence is becoming an substantial tool in load forecasting. Besides genetic algorithms, neural networks, fuzzy logic, wavelet networks and expert systems, the most important method is Support Vector Machine. This method uses machine learning to develop a model, which is then used for forecasting. In this thesis support vector machines are used for long term load forecasting in Krapina-Zagorje County. The model is developed using past load demand data and gross domestic product in Krapina-Zagorje County. Finally, results are shown for different methods of validation, and the prediction with the most probable results has been chosen.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:523601
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2018-02-06 08:04:13