Real-Time Implementation of Model Predictive Control for Cooling System of a Factory Building

• Published in: ASHRAE transactions Vol. 128; no. 2; pp. 175 - 182
• Main Authors: Ra, Seon-Jung, Kim, Jin-Hong, Jo, Hyeong-Gon, Kim, Young-Sub, Park, Cheol-Soo
• Format: Journal Article
• Language: English
• Published: Atlanta American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. (ASHRAE) 01.01.2022; American Society of Heating, Refrigeration and Air Conditioning Engineers, Inc
• Subjects: Accuracy; Air conditioning; Air temperature; Analysis; Artificial neural networks; Control systems; Cooling; Cooling systems; Diffusers; Energy conservation; Energy consumption; Energy management systems; Environmental control; Green buildings; Humidity; HVAC; Indoor environments; Machine learning; Neural networks; Predictive control; Real time; Relative humidity; Simulation; Simulation models; South Korea; Temperature; Variables; Weather forecasting; South Korea
• ISSN: 0001-2505

This paper presents a real-time application of model predictive control (MPC) for a cooling system of a target building using machine learning models. The building is a heavy-machinery assembly building consisting of many large indoor spaces. The size of a thermal zone of our interest is 80 m*60m *9.7m(262ft*197ft*32ft). The zone is served by four condensing units (210kW (59.7RT) * 4EA), two direct expansion type AHUs (40,000[m.sup.3]/h (1,412,588[ft.sup.3]/h) * 2EA) and 45 diffusers. To implement the MPC for the thermal zone, the authors developed two simulation models using Artificial Neural Network (ANN): one is to predict future indoor air temperatures at multiple indoor points with the inputs of current supply air temperature, outdoor air temperature, indoor air temperature, the status of condensing unit operation (on/off) and status of fan operation(on/off). The other is to predict future supply air temperature from AHUs with the inputs of current outdoor air temperature, the status of condensing unit operation (on/off) and status of fan operation, and outdoor air's relative humidity. The accuracy of the two models was assessed in terms of MBE and CVRMSE (MBE=1.2%, CVRMSE=3.8%). The objective function of MPC is to minimize the energy use of four condensing units while maintaining indoor temperature lower than or equal to the indoor setpoint temperature. The proposed MPC was realized in the target building at the sampling time of 10 minutes for four weeks in August-September 2021. It was found that the proposed control saved energy by 35.1%. Finally, the authors outline issues that could occur while performing the MPC of a factory building, as well as the potential of the MPC for indoor environmental control and energy savings.