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undergraduate thesis
Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:035959
Srića, Luka
University of Zagreb
Faculty of Electrical Engineering and Computing

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Srića, L. (2023). Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:035959

Srića, Luka. "Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:035959

Srića, Luka. "Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:035959

Srića, L. (2023). 'Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:035959

Srića L. Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:035959

L. Srića, "Metode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:035959

Metadata
TitleMetode strojnog učenja u predviđanju proizvodnje fotonaponskih elektrana
Title (english)Machine learning methods in forecasting photovoltaic power plant production
AuthorLuka Srića
MentorGoran Grdenić (mentor)
Committee memberGoran Grdenić (predsjednik povjerenstva)
Committee memberIvan Sudić (član povjerenstva)
Committee memberFranko Pandžić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Masovna integracija fotonaponskih elektrana u energetsku mrežu smatra se jednim od ključnih koraka u tranziciji prema održivom i ekološki prihvatljivom energetskom sustavu. Glavni izazov predstavlja činjenica da je proizvodnja solarne energije varijabilna jer ovisi o dostupnosti i količini sunčevog zračenja. Kako bi se smanjila nesigurnost i osigurala stabilnost sustava, potrebno je razviti precizne metode predviđanja proizvodnje fotonaponskih elektrana. Ovaj rad je fokusiran na rješavanje ovog problema korištenjem strojnog učenja. Predstavljene su metode strojnog učenja koje se najčešće koriste, objašnjena njihova teorijska osnova te su navedene prednosti takvog pristupa. Predložen je konkretan model LSTM neuronske mreže za predviđanje proizvodnje 1 sat unaprijed s vremenskom rezolucijom od 15 minuta. Performanse tog modela uspoređene su s performansama modela temeljenih na drugim metodama strojnog učenja.
Abstract (english)
Mass integration of photovoltaic power plants into the energy grid is considered one of the key steps in the transition towards a sustainable and environmentally friendly energy system. The biggest challenge lies in the inherent variability of solar energy production caused by its dependency on the availability of solar radiation. To reduce this uncertainty and ensure the stability of the system, it is necessary to develop precise photovoltaic power forecasting methods. This paper is focused on solving the problem through the application of machine learning. The most commonly used machine learning techniques are presented, their theoretical basis is explained, and the advantages of such an approach are listed. A concrete LSTM neural network model is proposed for forecasting production 1 hour ahead with a time resolution of 15 minutes. The performance of this model is then compared with the performance of models based on other machine learning methods.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:035959
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:33:52