Real-time model predictive cooling control for an HVAC system in a factory building

This paper presents the results of model predictive control (MPC) using multiple deep neural network (DNN) models for the cooling system of a factory building. The target building accommodates a large space (80 m x 60 m x 9.7 m) and serves cooling via 45 diffusers connected to a direct expansion air handling unit that comprises two air supply fans and four condensing units. The authors developed 10 simulation models using a DNN: One predicts the supply air temperature of the HVAC system, while the others predict the indoor temperature of nine zones. The models can sufficiently predict the thermal behavior of the HVAC system (normalized mean bias error (NMBE): -1.2 %, coefficient of variation of the root mean square error (CVRMSE): 3.8 %) and indoor environment (NMBE: 1.1 %, CVRMSE: 1.3 %). The purpose of the MPC is to minimize the energy consumption of the condensing units while maintaining the cooling set-point temperature. The MPC was applied to the target building for a sampling time of 10 min, for four weeks, from August 23 to September 17, 2021. It was found that energy consumption decreased by 35.1 % compared with the baseline period, while satisfying the cooling set-point temperature maintenance condition. Finally, the authors highlight that the DNN-based MPC is a practical approach sufficient for predicting indoor thermal behavior and has significant potential for energy saving.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

This paper presents the application results of model predictive control (MPC) using multiple deep neural network (DNN) models in the cooling system of a factory building. The authors developed 10 simulation models to predict the thermal behavior of the HVAC system and indoor environment. The MPC approach successfully reduces the energy consumption of the condensing units while maintaining the cooling set-point temperature.