| Abstract | As net-zero emerges as the critical issue, electrification is becoming important issue in building sector as well. Furthermore,
the cooling energy demand is sharply increasing due to the impact of global warming. To utilize expensive renewable energy
and to meet the increasing demand, the need for the energy efficiency and optimization in cooling system is significant
technology. Therefore, this paper presents a reinforcement learning model for the residential cooling system, which can adapt
to the residential environment within several days of observation (of occupant control of thermostats). The model utilizes
obtained occupant control data and the energy consumption data from the electricity meter attached to the air conditioner with
the neural network model to establish reasonable simulation-based training environment for the reinforcement learning agent,
enables the model can be applied in existing building quickly while it maintains the ability to dynamically adapt to the occupant
preference. The proposed model was validated in the chamber experiment, showed the energy saving witout sacrificing the
thermal preference of the occupant, proved with the sensation vote and the preference vote result. |
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