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This paper presents a real-time implementation of model predictive control (MPC) for HVAC systems in an ice-cream factory building. The target building consists of two large open spaces served by two HVAC systems. We developed four artificial neural network (ANN) models that predict the thermal states of the supply air and indoor air of the two thermal zones and prove to be accurate enough (MBE  = 2.65, CVRMSE = 9.43). The control variables employed in this study are the number of operating chillers, frequency of supply-air fan inverter and outdoor-air intake ratio. The objective function minimizes total energy use, and a constraint was set to maintain average indoor air temperatures close to set points. Real-time MPC was implemented at a sampling time of 20 min from 3 August to 30 August 2021 and could save approximately 31.7% of electricity when compared to the existing simple rule-based control.

Real-time predictive control of HVAC systems for factory building using lightweight data-driven model