20.500.12556/DKUM-81091 S strojnim učenjem podprta analiza vzorcev vektorizirane porabe električne energije magistrsko delo Machine learning-supported analysis of vectorized electricity consumption patterns V magistrskem delu raziskujemo smiselnost pretvorbe električne porabe hišnih naprav v večdimenzionalno vektorsko predstavitev za uporabo pri nadaljnjih raziskavah na področju analiziranja električne energije s tehnikami obdelave naravnega jezika. Slednje storimo s pomočjo postopka vdelave besed, ki ga izvedemo z umetno inteligenčnima tehnikama za obdelavo naravnega jezika Word2Vec in Doc2Vec. V empiričnem delu predlagamo metodo pretvorbe električne porabe v znakovne kategorije, pridobljene vektorje pa nadaljnje analiziramo s pomočjo treh tehnik strojnega učenja. Podrobneje se osredotočimo na iskanje električnih naprav s podobnimi vzorci porabe, določanje tipa električne naprave in napovedovanje porabe električne naprave. Raziskovanje zaključimo z odgovori na zastavljena raziskovalna vprašanja in potrditvijo pripadajočih hipotez ter glavne teze z dejstvom, da vektorizirana poraba električnih naprav zajame specifične vzorce porabe. In the master's thesis we investigate the feasibility of converting the electrical consumption of household appliances into a multidimensional vector representation for use in further research in the field of electricity analysis with natural language processing techniques. The latter is done with the help of the word embedding process, which is performed with artificially intelligent techniques for processing the natural language Word2Vec and Doc2Vec. In the empirical part, we propose a method of converting electrical consumption into character categories, and the obtained vectors are further analyzed with the help of three machine learning techniques. We focus in more detail on finding electrical devices with similar consumption patterns, determining the type of electrical device, and forecasting the consumption of an electrical device. We conclude the research with answers to the research questions and confirmation of the associated hypotheses and the main thesis with the fact that the vectorized consumption of electrical devices captures specific consumption patterns. električna energija vdelava besed Word2Vec Doc2Vec strojno učenje electricity word embedding Word2Vec Doc2Vec machine learning true true false [M. Pintarič] Slovenski jezik Angleški jezik Magistrsko delo/naloga 2022-01-03 20:14:46 2022-01-24 08:43:56 2022-04-02 11:39:55 0000-00-00 00:00:00 2022 0 Maribor 0 Maribor 1 spletni vir (1 datoteka PDF (XV, 107 f.)) 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.65+004.6.057.6(043.2) 98515971 MAG_Pintaric_Matic_2022.pdf MAG_Pintaric_Matic_2022.pdf 1 7E0DC4E619706FEB6F5C2ECB566213B2 500b55e2065578c335e098636946c66c227e398021602d6af11676797d9d34c1 9ea0e338-6cd1-11ec-92e4-00155d000105 20.500.12556/dkum/e0d300df-bebb-4df8-8b26-98640138b145 https://dk.um.si/Dokument.php?lang=slv&id=156011 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0