Abstract
The heat pump heating system has been widely accepted to provide space and water heating in commercial and residential buildings. However, the operating energy performance in practice conditions is limited due to the traditional control strategy. An efficient-cost advanced control strategy is desired to improve the overall operating performance. This paper presents a model predictive control (MPC) strategy for an air source heat pump (ASHP) heating system to optimize the overall operating performance. Based on the control-oriented model and objective function, MPC can provide the optimized compressor frequency and water mass flow rate in real-time, which can lead to the optimized real-time variable water temperature difference. Thus, the total power consumption can be minimized for the heating system by coordinating the compressor power consumption and water pump power consumption in the real-time operating condition. A high-fidelity physical model is first built to produce the required data source, and then the control-oriented model is derived based on the machine learning technique. MPC is designed to minimize the total power consumption providing that the heating capacity is well maintained. The MPC strategy is evaluated and compared with the traditional PI control method under three operating cases.
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