Deep Reinforcement Learning for Global Maximum Power Point Tracking: Design and Experiments in Real Photovoltaic Systems

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dc.contributor.authorJorge Felipe Gaviria
dc.contributor.authorMaría Isabella Torres
dc.contributor.authorLuis Felipe Giraldo
dc.contributor.authorCorinne Alonso
dc.contributor.authorMichaël Bressan
dc.coverage.spatialBolivia
dc.date.accessioned2026-03-22T20:48:58Z
dc.date.available2026-03-22T20:48:58Z
dc.date.issued2023
dc.identifier.doi10.2139/ssrn.4621061
dc.identifier.urihttps://doi.org/10.2139/ssrn.4621061
dc.identifier.urihttps://andeanlibrary.org/handle/123456789/84233
dc.language.isoen
dc.publisherRELX Group (Netherlands)
dc.relation.ispartofSSRN Electronic Journal
dc.sourceUniversidad de Los Andes
dc.subjectPhotovoltaic system
dc.subjectReinforcement learning
dc.subjectTracking (education)
dc.subjectPower (physics)
dc.subjectMaximum power point tracking
dc.subjectComputer science
dc.subjectPoint (geometry)
dc.subjectArtificial intelligence
dc.subjectReinforcement
dc.titleDeep Reinforcement Learning for Global Maximum Power Point Tracking: Design and Experiments in Real Photovoltaic Systems
dc.typepreprint

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