Autonomic Management of a Building's Multi-HVAC System Start-Up.
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2021
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IEEE Access
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Abstract
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 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.
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Keywords
Energy management, Heating, Ventilation and air conditioning systems, Autonomic computing, Machine learning, Multi-objective optimization, Smart building