Q-learning based model-free control method for indoor thermal environment of aged care building

Abstract

The present disclosure provides a Q-learning based model-free control method for an indoor thermal environment of an aged care building and belongs to the technical field of building environment control. According to the present disclosure, the monitored indoor temperatures of individual users and the heart rate and systolic pressure data of the aged are used as input data to a constructed Q-learning model, thus outputting a running control policy for a heating, ventilation and air conditioning system in the corresponding building. As a result, the control efficiency of the indoor temperature and the energy efficiency of the heating, ventilation and air conditioning system are improved. Compared with a traditional control model, the reinforced learning method based on the Q-learning theory can realize more accurate prediction on the cardiovascular health risk of the aged and can create a dynamic indoor thermal environment more suitable for the physical health of the aged.