Autonomic Management of a Building’s Multi-HVAC System Start-Up
| Autor: | Nuria Gallego-Salvador, José Antonio Gutiérrez de Mesa, José Manuel Gómez-Pulido, Maria Dolores R.-Moreno, Alberto Garces-Jimenez, Jose Aguilar |
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| Přispěvatelé: | Universidad de Alcalá. Departamento de Automática, Universidad de Alcalá. Departamento de Ciencias de la Computación |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Computer science Ventilation and air conditioning systems 020209 energy Autonomic computing 02 engineering and technology 010501 environmental sciences 01 natural sciences Multi-objective optimization Task (project management) Setpoint Heating Machine learning 0202 electrical engineering electronic engineering information engineering General Materials Science 0105 earth and related environmental sciences Building automation Informática business.industry General Engineering Energy management Control reconfiguration Control engineering Automation Smart building Beating Task analysis business |
| Zdroj: | IEEE Access DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria instname e_Buah Biblioteca Digital Universidad de Alcalá Universidad de Alcalá (UAH) DDFV: Repositorio Institucional de la Universidad Francisco de Vitoria Universidad Francisco de Vitoria |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3078550 |
| Popis: | Most 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 defined operational setpoint, and if that is not the case, another task for reconfiguring the operational mode of the multi-HVAC system to redirect it. The first task uses machine learning techniques to build detection and prediction models, and the second task defines a reconfiguration 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. European Commission Junta de Comunidades de Castilla-La Mancha Agencia Estatal de Investigación |
| Databáze: | OpenAIRE |
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