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Title:Primerjava modelov za napovedovanje porabe električne energije : diplomsko delo
Authors:ID Novak, Nik (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Kohek, Štefan (Comentor)
Files:.pdf UN_Novak_Nik_2019.pdf (2,02 MB)
MD5: 796820BB486CA408CDFE87F5693F4733
PID: 20.500.12556/dkum/cf0045e1-72ec-49fa-85a5-e49bf68ff7a9
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Uspešno napovedovanje porabe električne energije je pomembno z vidika ohranitve planeta, saj zaradi ustvarjanja viška porabljamo vire brez razloga. V diplomskem delu smo primerjali dva modela za napovedovanje porabe električne energije, in sicer nevronsko mrežo LSTM in model SARIMA za napovedovanje vrednosti v časovnih vrstah. Za testiranje modelov so bili uporabljeni podatki v tedenski ločljivosti, pridobljeni od podjetja Maked Energea, d. o. o. V rezultatih se je nevronska mreža LSTM pri uporabljenih nizih podatkov izkazala kot najboljša.
Keywords:strojno učenje, LSTM, RNN, SARIMA, napovedovanje, poraba električne energije.
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[N. Novak]
Year of publishing:2019
Number of pages:VI, 32 str.
PID:20.500.12556/DKUM-74887 New window
UDC:621.311.68(043.2)
COBISS.SI-ID:22786582 New window
NUK URN:URN:SI:UM:DK:EUP0NNLP
Publication date in DKUM:21.11.2019
Views:1233
Downloads:141
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:11.09.2019

Secondary language

Language:English
Title:Comparison of models for electricity consumption forecasting
Abstract:Electricity consumption forecast is important for conservation of Earth, as excess energy production is using the resources for no reason. In this thesis, we compared two models for predicting power consumption, namely the LSTM neural network and the SARIMA model for time series forecasting. Tests were performed on weekly resolution data obtained from Maked Energea, d.o.o. In the results, the LSTM neural network showed the best performance on the used datasets.
Keywords:machine learning, LSTM, RNN, SARIMA, forecasting, energy consumption.


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