Mostrar el registro sencillo del ítem

dc.contributor.authorAguilar, Jose
dc.contributor.authorGarcés Jiménez, Alberto
dc.contributor.authorGómez Pulido, José Manuel
dc.contributor.authorRodríguez Moreno, María Dolores
dc.contributor.authorGutiérrez de Mesa, José Antonio
dc.contributor.authorGallego Salvador, Nuria
dc.date.accessioned2021-06-18T10:44:12Z
dc.date.available2021-06-18T10:44:12Z
dc.date.issued2021
dc.identifier.issn2169-3536spa
dc.identifier.urihttp://hdl.handle.net/10641/2339
dc.description.abstractMost studies about the control, automation, optimization and supervision of building HVAC systems concentrate on the steady-state regime, i.e., when the equipment is already working at its setpoints. The originality of the current work consists of proposing the optimization of building multi-HVAC systems from start-up until they reach the setpoint, making the transition to steady state-based strategies smooth. The proposed approach works on the transient regime of multi-HVAC systems optimizing contradictory objectives, such as the desired comfort and energy costs, based on the ``Autonomic Cycle of Data Analysis Tasks'' concept. In this case, the autonomic cycle is composed of two data analysis tasks: one for determining if the system is going towards the de ned operational setpoint, and if that is not the case, another task for recon guring the operational mode of the multi-HVAC system to redirect it. The rst task uses machine learning techniques to build detection and prediction models, and the second task de nes a recon guration model using multiobjective evolutionary algorithms. This proposal is proven in a real case study that characterizes a particular multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is impressive since there is a high level of conformity for the multi-HVAC system to reach the setpoint and deliver the operation to the steady-state smoothly, avoiding overshooting and other non-desirable transitional effects.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.subjectEnergy managementspa
dc.subjectHeatingspa
dc.subjectVentilation and air conditioning systemsspa
dc.subjectAutonomic computingspa
dc.subjectMachine learningspa
dc.subjectMulti-objective optimizationspa
dc.subjectSmart buildingspa
dc.titleAutonomic Management of a Building's Multi-HVAC System Start-Up.spa
dc.typejournal articlespa
dc.type.hasVersionAMspa
dc.rights.accessRightsopen accessspa
dc.description.extent6073 KBspa
dc.identifier.doi10.1109/ACCESS.2021.3078550spa
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9426915spa


Ficheros en el ítem

FicherosTamañoFormatoVer
1.- Autonomic Management of a ...5.930MbPDFVer/

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución-NoComercial-SinDerivadas 3.0 España
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 3.0 España