Development of prediction-based operation planning method for domestic air-conditioner with adaptive learning of installation environment

The world is more aware of the need for saving energy because of increasing world energy consumption and environmental problems. To promote saving energy in the domestic field, the use of home energy management systems (HEMSs) is rapidly spreading. The HEMS which has automatically controlling function can control domestic electrical appliances including air-conditioners (ACs). In this research, we focus on AC operation plans to improve thermal comfort and reduce electricity costs for residents. However, AC control planning is generally a difficult task because the operation results greatly depend on the environmental characteristics in which the HEMS is installed. To solve this problem, we proposed an AC planning method that accounts for environmental characteristics and uncertainty in prediction by using historical data.