Deep learning optimal control for a solar-biomass system for residential buildings

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dc.contributor.authorZsembinszki, Gabriel
dc.contributor.authorFernàndez Camon, César
dc.contributor.authorBorri, Emiliano
dc.contributor.authorCabeza, Luisa F.
dc.date.accessioned2022-11-15T11:54:40Z
dc.date.available2022-11-15T11:54:40Z
dc.date.issued2022
dc.description.abstractNowadays, it is well known that the reduction of energy consumption in buildings is crucial to achieve a substantial reduction of gas emission to the atmosphere and decrease the fast depletion of energy sources. Indeed, buildings are responsible for almost 40% of the overall energy consumption and gas emission into the atmosphere [1], therefore, immediate actions are needed. The reduction of the energy demand through passive solutions (i.e. building envelope) has been taking into account in directives such as the Energy Performance of Buildings Directive [2]. However, the generation of energy through efficient systems and the use of renewable sources is also required to achieve deep decarbonisation of the grid. However, the main disadvantage of renewable sources is represented by their daily and seasonal intermittency. Therefore, a system that allows the combination of more of them can provide a higher flexibility and increase the total share of renewables to meet energy needs in buildings.ca_ES
dc.description.sponsorshipThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 814945 (SolBio-Rev). This work was partially funded by Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE), Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T - MCIU/AEI). This work is partially supported by ICREA under the ICREA Academia programme. The 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.ca_ES
dc.format.extent9 p.ca_ES
dc.identifier.urihttp://hdl.handle.net/10459.1/84229
dc.language.isoengca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093849-B-C31/ES/METODOLOGIA PARA EL ANALISIS DE TECNOLOGIAS DE ALMACENAMIENTO DE ENERGIA TERMICA HACIA UNA ECONOMIA CIRCULAR/ca_ES
dc.relationinfo:eu-repo/grantAgreement/MICIU//RED2018-102431-T/ES/RED ESPAÑOLA EN ALMACENAMIENTO DE ENERGIA TERMICA/ca_ES
dc.relation.ispartofXII National and III International Conference on Engineering Thermodynamics. 29 June – 1 July 2022, Madrid, Spainca_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/814945/EU/SolBio-Revca_ES
dc.rights© 2022 GREiA, University of Lleidaca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.subjectDeep reinforcement learningca_ES
dc.subjectOptimal controlca_ES
dc.subjectSmart controlca_ES
dc.subjectModelingca_ES
dc.subjectResidential buildingsca_ES
dc.subjectHybrid energy systemca_ES
dc.subjectSolar energyca_ES
dc.subjectBiomassca_ES
dc.titleDeep learning optimal control for a solar-biomass system for residential buildingsca_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectca_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_ES
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