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Title:Primerjava pristopov k napovedovanju porabe električne energije
Authors:ID Šmigoc, Sabina (Author)
ID Bokal, Drago (Mentor) More about this mentor... New window
ID Delčnjak, Milena (Comentor)
Files:.pdf MAG_Smigoc_Sabina_2016.pdf (1,38 MB)
MD5: AB0A6B212EC5EBD8D3000EC05BDB80B2
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:Magistrsko delo obravnava primerjavo pristopov k napovedovanju porabe električne energije. Delo je razdeljeno na štiri dele. V prvem poglavju so predstavljeni osnovni statistični koncepti, ki so potrebni za razumevanje opravljenih analiz in primerjav, to so: slučajna spremenljivka, statistična populacija in vzorec, statistični test, srednje vrednosti, mere variabilnosti in časovne vrste. Pregledu matematičnega področja sledi poglavje, kjer so predstavljene tehnike modeliranja porabe električne energije. Predstavljene so mere kakovosti modelov ter pet metod modeliranja: večstopenjska linearna regresija, umetne nevronske mreže, metoda podpornih vektorjev, eksponentno glajenje in metoda glavnih komponent. V tretjem poglavju je povzet pregled uporabe taksonomij, izdelana je taksonomija napovedovanja v elektrogospodarstvu, predstavljen je pregled domenske literature in prikazana je uvrstitev pristopov iz člankov v izdelano taksonomijo. Glavni rezultat poglavja je graf primerjav tehnik modeliranja, ki smo jih zasledili v literaturi. V zadnjem poglavju so predstavljeni primeri modeliranja porabe električne energije. Najprej je predstavljena korelacijska analiza osnovnih in izpeljanih atributov s časovno vrsto porabe električne energije, nato so prikazani rezultati implementiranih modelov napovedovanja, njihove natančnosti in finančne učinkovitosti.
Keywords:napovedovanje, korelacijska analiza, linearna regresija, podporni vektorji, umetne nevronske mreže, eksponentno izravnavanje, analiza glavnih komponent, taksonomija.
Place of publishing:Maribor
Publisher:[S. Šmigoc]
Year of publishing:2016
PID:20.500.12556/DKUM-57593 New window
UDC:519.233(043.2)
COBISS.SI-ID:22050824 New window
NUK URN:URN:SI:UM:DK:KXZTZUM1
Publication date in DKUM:29.03.2016
Views:3131
Downloads:369
Metadata:XML DC-XML DC-RDF
Categories:FNM
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Secondary language

Language:English
Title:Comparison of approaches to forecasting electricity consumption
Abstract:This master thesis deals with the comparison of approaches to forecasting electricity consumption. The thesis is divided into four parts. In the first chapter, we present the basic statistical concepts, which are necessary for understanding the analyses and comparisons, these are: random variable, statistical population and statistical sample, statistical test, the mean values, measures of statistical dispersion, and time series. Review of mathematical fields is followed by a section, which presents the modelling techniques of electricity consumption. It presents the measures of quality of models and five modelling techniques: multiple linear regression, artificial neural networks, support vector machines, exponential smoothing and principal components analysis. In the third chapter, we review the use of taxonomies, design a taxonomy to predict the electricity consumption, present an overview of the domain literature and show the classification of the approaches set out of literature into our taxonomy. The main result of the chapter is a graph of comparisons of modelling techniques. In the last chapter, we present examples of modelling electricity consumption. First we make a correlation analysis of basic and derived attributes of a time series of electricity consumption, then we present five different implemented forecasting models, their qualities and financial efficiencies.
Keywords:Keywords: forecasting, correlation analysis, multiple linear regression, support vector machine, artificial neural networks, exponential smoothing, principal component analysis, taxonomy.


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