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dc.contributor.authorZsembinszki, Gabriel
dc.contributor.authorFernàndez Camon, César
dc.contributor.authorVérez, David
dc.contributor.authorCabeza, Luisa F.
dc.date.accessioned2021-05-07T07:59:49Z
dc.date.available2021-05-07T07:59:49Z
dc.date.issued2021
dc.identifier.issn2075-5309
dc.identifier.urihttp://hdl.handle.net/10459.1/71246
dc.description.abstractDeep Reinforcement Learning (DRL) proved to be successful for solving complex control problems and has become a hot topic in the field of energy systems control, but for the particular case of thermal energy storage (TES) systems, only a few studies have been reported, all of them with a complexity degree of the TES system far below the one of this study. In this paper, we step forward through a DRL architecture able to deal with the complexity of an innovative hybrid energy storage system, devising appropriate high-level control operations (or policies) over its subsystems that result optimal from an energy or monetary point of view. The results show that a DRL policy in the system control can reduce the system operating costs by more than 50%, as compared to a rule-based control (RBC) policy, for cooling supply to a reference residential building in Mediterranean climate during a period of 18 days. Moreover, a robustness analysis was carried out, which showed that, even for large errors in the parameters of the system simulation models corresponding to an error multiplying factors up to 2, the average cost obtained with the original model deviates from the optimum value by less than 3%, demonstrating the robustness of the solution over a wide range of model errors.
dc.description.sponsorshipThe authors would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.3390/buildings11050194
dc.relation.ispartofBuildings, 2021, vol. 11, núm. 5, p. 194-1-194-31
dc.rightscc-by (c) Gabriel Zsembinszki et al., 2021
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep reinforcement learning
dc.subjectOptimal control
dc.subjectOptimization
dc.subjectHYBUILD
dc.subjectThermal energy storage
dc.subjectResidential buildings
dc.titleDeep learning optimal control for a complex hybrid energy storage system
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2021-05-07T07:59:49Z
dc.identifier.idgrec031284
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.3390/buildings11050194


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cc-by (c) Gabriel Zsembinszki et al., 2021
Except where otherwise noted, this item's license is described as cc-by (c) Gabriel Zsembinszki et al., 2021