{"?xml":{"@version":"1.0"},"edm:RDF":{"@xmlns:dc":"http://purl.org/dc/elements/1.1/","@xmlns:edm":"http://www.europeana.eu/schemas/edm/","@xmlns:wgs84_pos":"http://www.w3.org/2003/01/geo/wgs84_pos","@xmlns:foaf":"http://xmlns.com/foaf/0.1/","@xmlns:rdaGr2":"http://rdvocab.info/ElementsGr2","@xmlns:oai":"http://www.openarchives.org/OAI/2.0/","@xmlns:owl":"http://www.w3.org/2002/07/owl#","@xmlns:rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","@xmlns:ore":"http://www.openarchives.org/ore/terms/","@xmlns:skos":"http://www.w3.org/2004/02/skos/core#","@xmlns:dcterms":"http://purl.org/dc/terms/","edm:WebResource":[{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-3143OSOT/95422080-56ac-4b39-b1ff-f3beeba1d14a/PDF","dcterms:extent":"520 KB"},{"@rdf:about":"http://www.dlib.si/stream/URN:NBN:SI:DOC-3143OSOT/0df146c3-9a6d-4495-8170-effd189ddcca/TEXT","dcterms:extent":"0 KB"}],"edm:ProvidedCHO":{"@rdf:about":"URN:NBN:SI:DOC-3143OSOT","dcterms:issued":"2026","dc:creator":["Meža, Marko","Sivec, Andraž"],"dc:format":[{"@xml:lang":"sl","#text":"številka:1/2"},{"@xml:lang":"sl","#text":"letnik:93"},{"@xml:lang":"sl","#text":"str. 63-68"}],"dc:identifier":["ISSN:0013-5852","COBISSID_HOST:279259395","URN:URN:NBN:SI:doc-3143OSOT"],"dc:language":"sl","dc:publisher":{"@xml:lang":"sl","#text":"Elektrotehniška zveza Slovenije"},"dc:source":{"@xml:lang":"sl","#text":"Elektrotehniški vestnik"},"dc:subject":[{"@xml:lang":"en","#text":"algorithm comparison"},{"@xml:lang":"en","#text":"green transition"},{"@xml:lang":"sl","#text":"optimizacija krmiljenja baterije po ceni"},{"@xml:lang":"en","#text":"optimization of battery control based on price"},{"@xml:lang":"sl","#text":"primerjava algoritmov"},{"@xml:lang":"en","#text":"reinforcement learning"},{"@xml:lang":"sl","#text":"spodbujevalno učenje"},{"@xml:lang":"sl","#text":"zeleni prehod"}],"dc:title":{"@xml:lang":"sl","#text":"SARSA ali DQN za krmiljenje baterijskega hranilnika|"},"dc:description":[{"@xml:lang":"sl","#text":"The increasing share of solar and wind energy production introduces challenges related to excess electricity generation and its negative prices, which affect the stability of power systems. Battery storage is emerging as a promising solution, but its optimal operation requires advanced control strategies. This paper compares two reinforcement learning methods for battery management: the tabular SARSA and the deep QNetwork (DQN) method. Both methods are trained on historical data from a photovoltaic plant and electricity market prices, with the objective of minimizing energy purchase costs. The results demonstrate that both methods significantly reduce the costs compared to the baseline without storage, with DQN outperforming SARSA due to its ability to capture complex patterns and handle continuous data input. The findings confirm that reinforcement learning can effectively support decision-making in energy storage management, contributing to improved economic efficiency and system stability while also highlighting some implementation challenges, such as training instability and sensitivity to hyperparameters"},{"@xml:lang":"sl","#text":"Zaradi porasta proizvodnje električne energije iz sončnih in vetrnih virov se pojavlja problem viškov generacije in negativnih cen elektrike, kar vpliva na stabilnost elektroenergetskega sistema. Kot možna rešitev se vse bolj uveljavlja uporaba baterijskih hranilnikov, ki pa zahtevajo učinkovito upravljanje za najboljši učinek. V članku primerjamo delovanje dveh metod spodbujevalnega učenja pri optimizaciji delovanja baterije: tabelarično metodo SARSA in metodo z nevronsko mrežo DQN. Agenta se učita na podlagi zgodovinskih podatkov o proizvodnji sončne elektrarne, porabi gospodinjstva in tržnih cenah elektrike, njun cilj pa je minimizacija stroškov nakupa energije. Rezultati kažejo, da uporaba obeh pristopov bistveno zmanjša stroške v primerjavi s primerom brez baterije. DQN dosega najboljše rezultate, saj se bolje prilagodi kompleksnim vzorcem v podatkih in boljše deluje v večjih prostorih stanj. Raziskava potrjuje, da lahko spodbujevalno učenje učinkovito podpira odločanje pri upravljanju hranilnikov in prispeva k večji ekonomski učinkovitosti ter stabilnosti energetskega ˇ sistema ter prikaze nekatere izzive pri implementaciji"}],"edm:type":"TEXT","dc:type":[{"@xml:lang":"sl","#text":"znanstveno časopisje"},{"@xml:lang":"en","#text":"journals"},{"@rdf:resource":"http://www.wikidata.org/entity/Q361785"}]},"ore:Aggregation":{"@rdf:about":"http://www.dlib.si/?URN=URN:NBN:SI:DOC-3143OSOT","edm:aggregatedCHO":{"@rdf:resource":"URN:NBN:SI:DOC-3143OSOT"},"edm:isShownBy":{"@rdf:resource":"http://www.dlib.si/stream/URN:NBN:SI:DOC-3143OSOT/95422080-56ac-4b39-b1ff-f3beeba1d14a/PDF"},"edm:rights":{"@rdf:resource":"http://rightsstatements.org/vocab/InC/1.0/"},"edm:provider":"Slovenian National E-content Aggregator","edm:intermediateProvider":{"@xml:lang":"en","#text":"National and University Library of Slovenia"},"edm:dataProvider":{"@xml:lang":"sl","#text":"Elektrotehniška zveza Slovenije"},"edm:object":{"@rdf:resource":"http://www.dlib.si/streamdb/URN:NBN:SI:DOC-3143OSOT/maxi/edm"},"edm:isShownAt":{"@rdf:resource":"http://www.dlib.si/details/URN:NBN:SI:DOC-3143OSOT"}}}}