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undergraduate thesis
Primjena strojnog učenja u energetskim sustavima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:799376
Smolić-Ročak, Magda
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Smolić Ročak, M. (2022). Primjena strojnog učenja u energetskim sustavima (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:799376

Smolić Ročak, Magda. "Primjena strojnog učenja u energetskim sustavima." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:799376

Smolić Ročak, Magda. "Primjena strojnog učenja u energetskim sustavima." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:799376

Smolić Ročak, M. (2022). 'Primjena strojnog učenja u energetskim sustavima', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 16 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:799376

Smolić Ročak M. Primjena strojnog učenja u energetskim sustavima [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 November 16] Available at: https://urn.nsk.hr/urn:nbn:hr:168:799376

M. Smolić Ročak, "Primjena strojnog učenja u energetskim sustavima", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:799376

Metadata
TitlePrimjena strojnog učenja u energetskim sustavima
Title (english)Machine learning application in power system
AuthorMagda Smolić-Ročak
MentorBojan Trkulja (mentor)
Committee memberBojan Trkulja (predsjednik povjerenstva)
Committee memberAna Drandić (član povjerenstva)
Committee memberStjepan Frljić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-08-29, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema ovog rada jest primjena strojnog učenja u energetskim sustavima realizirana programskim jezikom Python. Sustavi koji se bave proizvodnjom energije nastoje ostvariti optimalnu energetsku učinkovitost. S tim ciljem, rad se bavi korištenjem različitih algoritama i modela strojnog učenja u svrhu predviđanja potrošnji energije. Na ulaz modela se dovode informacije o temperaturama i količinama vlažnosti, a na izlazu model generira predviđenu potrošnju energije. Jednostavna regresija i višestruka linearna regresija su korištene za izračun predviđene kumulativne satne i dnevne potrošnje energije. Korištenjem neuronske mreže, izgrađen je model za izračun i prikaz aproskimirane funkcije satnih potrošnji energije tijekom jednog dana. Uz dobivene informacije o budućim potrošnjama, elektrane mogu unaprijed pretpostaviti koliku količinu energije će generirati u odabrano vrijeme.
Abstract (english)
The topic of this paper is the application of machine learning in energy systems. Systems that deal with energy production strive to achieve optimal energy efficiency. To this end, the paper deals with the use of different algorithms and models of machine learning for the purpose of predicting energy consumption. At the input of the model, information on temperatures and amounts of humidity is brought, and at the output, the model generates the predicted energy consumption. Simple regression and multiple linear regression were used to calculate the predicted cumulative hourly and daily energy consumption. Using the neural network, a model was built to calculate and display the approximated function of hourly energy consumption over one day. With the obtained information on future consumption, power plants can predict in advance how much energy they will generate at the selected time.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:799376
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 on2023-01-15 11:13:13