A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period

Air-conditioning systems in commercial buildings are usually switched on in advance to precool the indoor spaces to create an acceptable working environment upon the office hour. However, the central cooling systems often fail to provide enough cooling supply capacity due to the high cooling demand at the morning start period especially in hot seasons. In this situation, the imbalanced cooling distribution in the air-conditioning systems often results in large difference of cooling-down speed among different building zones, so that the precooling time has to be extended, leading to significant energy waste. This study proposes a new iterative learning control strategy to properly manage the cooling distribution (i.e., water valve openings of air-handling units) for achieving uniform cooling (i.e., synchronously reaching the indoor dry-blub temperature setpoint) among building zones during the morning start period. A reinforcement learning method (Q-learning) is adopted for the control parameter setting of the developed iterative learning controller. Validation tests are conducted and results show that the proposed control strategy could reduce the daily precooling time up to 12.1% during typical days in Hong Kong by achieving uniform cooling. The daily energy consumption could be reduced between 5.1% and 17.8% by shortening morning start period, corresponding a weekly electrical energy saving between 1,376 kWh and 2,916 kWh in the test building.