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Title:Razvoj modela za napovedovanje odjema toplote v sistemu daljinskega ogrevanja z uporabo umetne inteligence : magistrsko delo
Authors:ID Jakopiček, Patrik (Author)
ID Klančnik, Simon (Mentor) More about this mentor... New window
ID Crnogaj, Katja (Mentor) More about this mentor... New window
ID Kokalj, Filip (Comentor)
ID Kolman, Alen (Comentor)
Files:.pdf MAG_Jakopicek_Patrik_2025.pdf (8,05 MB)
MD5: 00815AB8267BBC2C54E3C01A35B59367
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Magistrsko delo obravnava razvoj različnih modelov globokih nevronskih mrež za napovedovanje odjema toplote v sistemu daljinskega ogrevanja. Razviti so bili trije napovedni modeli, pri čemer ima vsak svoje prednosti in slabosti. Predstavljen je celoten postopek razvoja – od zbiranja in urejanja podatkov, izbire ustreznih slojev in arhitekture nevronskih mrež, do določitve hiperparametrov učenja. Za vsako kombinacijo modelov so prikazane tudi metrične napake. Poleg tega so ocenjeni prihranki oziroma dodatni stroški ob uporabi posameznih napovedi. Rezultati so pokazali, da razviti modeli v nekaterih primerih podajo boljšo napoved od trenutno uporabljenega sistema napovedovanja in z njimi lahko prispevamo k optimizaciji stroškov. V primerih, kjer zaostajajo, pa nam razviti modeli ponujajo dobro izhodišče za nadaljnji razvoj in izboljšave.
Keywords:daljinski sistem ogrevanja, globoko učenje, LSTM, programsko napovedovanje
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[P. Jakopiček]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XIV, 94 f.))
PID:20.500.12556/DKUM-92762 New window
UDC:[004.8.032.26:519.216]:697.34(043.2)
COBISS.SI-ID:239581443 New window
Publication date in DKUM:27.05.2025
Views:151
Downloads:84
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:12.05.2025

Secondary language

Language:English
Title:Development of a Model for Heat Demand Prediction in a District Heating System Using Artificial Intelligence
Abstract:The master's thesis addresses the development of various deep neural network models for predicting heat demand in a district heating system. Three predictive models were developed, each with their own advantages and disadvantages. The entire development process is presented – from data collection and preprocessing, selection of appropriate layers and neural network architectures, to the determination of learning hyperparameters. For each model combination, metric errors are also presented. In addition, estimated savings or additional costs associated with using each prediction are evaluated. The results showed that in some cases, the developed models provide better predictions than the currently used forecasting system and can contribute to cost optimization. In cases where they underperform, the developed models offer a solid foundation for further development and improvement.
Keywords:District heating system, deep learning, LSTM, software prediction


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