Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings

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dc.citation.journalTitleIEEE Accesseng
dc.contributor.authorMorales Escobar L.
dc.contributor.authorAguilar J.
dc.contributor.authorGarces-Jimenez A.
dc.contributor.authorGutierrez De Mesa J.A.
dc.contributor.authorGomez-Pulido J.M.
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorMorales Escobar L.
dc.creatorAguilar J.
dc.creatorGarces-Jimenez A.
dc.creatorGutierrez De Mesa J.A.
dc.creatorGomez-Pulido J.M.
dc.date.accessioned2021-04-12T20:55:48Z
dc.date.available2021-04-12T20:55:48Z
dc.date.issued2020-01-01
dc.description.abstractControl in HVAC (heating, ventilation and air-conditioning) systems of buildings is not trivial, and its design is considered challenging due to the complexity in the analysis of the dynamics of its nonlinear characteristics for the identification of its mathematical model. HVAC systems are complex since they consist of several elements, such as heat pumps, chillers, valves, heating/cooling coils, boilers, air-handling units, fans, liquid/air distribution systems, and thermal storage systems. This article proposes the application of LAMDA (learning algorithm for multivariable data analysis) for advanced control in HVAC systems for buildings. LAMDA addresses the control problem using a fuzzy classification approach without requiring a mathematical model of the plant/system. The method determines the degree of adequacy of a system for every class and subsequently determines its similarity degree, and it is used to identify the functional state or class of the system. Then, based on a novel inference method that has been added to LAMDA, a control action is computed that brings the system to a zero-error state. The LAMDA controller performance is analyzed via evaluation on a regulation problem of an HVAC system of a building, and it is compared with other similar approaches. According to the results, our method performs impressively in these systems, thereby leading to a trustable model for the implementation of improved building management systems. The LAMDA control performs very well for disturbances by proposing control actions that are not abrupt, and it outperforms the compared approaches. © 2013 IEEE.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=10280
dc.identifier.doi10.1109/ACCESS.2020.2966545
dc.identifier.issn21693536
dc.identifier.otherWOS;000524751800002
dc.identifier.otherSCOPUS;2-s2.0-85079780448
dc.identifier.urihttp://hdl.handle.net/10784/28636
dc.language.isoengeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationDOI;10.1109/ACCESS.2020.2966545
dc.relationWOS;000524751800002
dc.relationSCOPUS;2-s2.0-85079780448
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079780448&doi=10.1109%2fACCESS.2020.2966545&partnerID=40&md5=1f8864918b31c8b43cbeecb10ff6b776
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/2169-3536
dc.sourceIEEE Access
dc.subjectAir conditioningeng
dc.subjectArtificial intelligenceeng
dc.subjectComputer circuitseng
dc.subjectControl engineeringeng
dc.subjectDigital storageeng
dc.subjectFuzzy logiceng
dc.subjectHeat storageeng
dc.subjectIntelligent buildingseng
dc.subjectBuilding management systemeng
dc.subjectController performanceeng
dc.subjectFuzzy classificationeng
dc.subjectHVAC controleng
dc.subjectLAMDAeng
dc.subjectLearning algorithm for multivariable data analysiseng
dc.subjectNonlinear characteristicseng
dc.subjectThermal storage systemeng
dc.subjectHVACeng
dc.titleAdvanced fuzzy-logic-based context-driven control for HVAC management systems in buildingseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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