Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings
| Autor: | Alberto Garces-Jimenez, José Manuel Gómez-Pulido, Álvaro J. García-Tejedor, Jose Aguilar, José Antonio Gutiérrez de Mesa, Nuria Gallego-Salvador |
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| Rok vydání: | 2019 |
| Předmět: |
Chiller
General Computer Science Computer science 020209 energy data analysis Building management systems smart building Autonomic computing Data analysis multi-chiller ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology 010501 environmental sciences 01 natural sciences Refrigerant HVAC system HVAC 0202 electrical engineering electronic engineering information engineering General Materials Science 0105 earth and related environmental sciences Building automation Building management system business.industry General Engineering Control engineering Energy consumption Smart building Multi-objective optimization autonomic computing multi-objective optimization lcsh:Electrical engineering. Electronics. Nuclear engineering business lcsh:TK1-9971 |
| Zdroj: | DDFV: Repositorio Institucional de la Universidad Francisco de Vitoria Universidad Francisco de Vitoria IEEE Access, Vol 7, Pp 123402-123415 (2019) DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria instname |
| Popis: | This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems. post-print 4413 KB |
| Databáze: | OpenAIRE |
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