Browsing by Author "Aguilar, Jose"
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Item An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building.(Energies, 2020) Aguilar, Jose; Ardila, Douglas; Avendaño, Andrés; Macías, Felipe; White, Camila; Gómez Pulido, José; Gutiérrez de Mesa, José; Garcés Jiménez, AlbertoEarly fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’sHVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.Item Autonomic Management of a Building's Multi-HVAC System Start-Up.(IEEE Access, 2021) Aguilar, Jose; Garcés Jiménez, Alberto; Gómez Pulido, José Manuel; Rodríguez Moreno, María Dolores; Gutiérrez de Mesa, José Antonio; Gallego Salvador, NuriaMost 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.