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Title:Napovedovanje porabe električne energije z rekurentnimi nevronskimi mrežami : magistrsko delo
Authors:Kos, Urban (Author)
Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Kos_Urban_2020.pdf (7,12 MB)
MD5: C50D4E8183550B0BB18AE99864FB3F85
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Predvidevanje porabe električne energije predstavlja zelo pomemben člen v elektroenergetski industriji, saj lahko pripomore k optimizaciji proizvodnje. S pomočjo strojnega učenja, natančneje rekurentnih nevronskih mrež, je mogoče natančno napovedati električno energijo. Veliko vlogo pri napovedovanju igrajo kakovost in količina podatkov ter arhitektura in nastavitve nevronske mreže. V teoretičnem delu je podrobno opisana nevronska mreža in njeni osnovni gradniki, kjer je bilo največ pozornosti posvečene rekurentnim mrežam, praktični del pa prikazuje izvedbo eksperimenta napovedovanja porabe električne energije z rekurentnimi nevronskimi mrežami z različno arhitekturo in podatki.
Keywords:rekurentne nevronske mreže, električna energija, napovedovanje električne energije
Year of publishing:2020
Place of performance:Maribor
Publisher:[U. Kos]
Number of pages:XII, 124 str.
Source:Maribor
UDC:[004.832:519.216]:621.31(043.2)
COBISS_ID:27115523 New window
NUK URN:URN:SI:UM:DK:1MSOEKSP
Views:315
Downloads:66
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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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:22.04.2020

Secondary language

Language:English
Title:Predicting power consumption with recurrent neural networks
Abstract:The anticipation of electricity consumption represents a very important link in the electrical energy industry as it can help optimize production. With the help of machine learning, more precisely recurrent neural networks, electricity can be accurately predicted. The quality and quantity of data, as well as the architecture and settings of the neural network play a big role in forecasting. The theoretical part describes in detail the neural network and its basic building blocks, where the greatest attention was paid to the recurrent parts of the network and the practical part shows the implementation of an experiment for the prediction of electricity consumption with recurrent neural networks with different architecture and data.
Keywords:recurrent neural networks, electricity, electricity forecasting


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