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dc.contributor.authorMorales Escobar, L.
dc.contributor.authorAguilar, J.
dc.contributor.authorGarcés Jiménez, Alberto
dc.contributor.authorGutiérrez de Mesa, José Antonio
dc.contributor.authorGómez Pulido, José Manuel
dc.date.accessioned2020-02-07T11:18:13Z
dc.date.available2020-02-07T11:18:13Z
dc.date.issued2020
dc.identifier.issn2169-3536spa
dc.identifier.urihttp://hdl.handle.net/10641/1851
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 identi cation 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 classi cation 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 toLAMDA, 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.spa
dc.language.isoengspa
dc.publisherIEEE Accessspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectHVAC controlspa
dc.subjectControl engineeringspa
dc.subjectFuzzy logicspa
dc.subjectArtificial intelligencespa
dc.titleAdvanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings.spa
dc.typearticlespa
dc.description.versionpost-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent3259 KBspa
dc.identifier.doi10.1109/ACCESS.2020.2966545spa
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8959189spa


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