강화학습 기반냉방 자율제어 모델의 기축 공동주택 상 적용성 평가
| 주 저자 | 김세헌 |
|---|---|
| 공동 저자 | 김태연, 남영도, 정재원 |
| 소속 | - |
| Abstract | This study proposes a reinforcement learning(RL) model for residential cooling control. The proposed model derives a preferred temperature range from thermostat control data of the occupant. A neural network estimates the preference range, which is then used to define the reward function. The model was evaluated in a chamber experiment simulating real residential conditions by comparing measured thermal sensation and preference votes. Results show that proposed model achieved similar thermal preference level to occupant-controlled case, while reducing energy consumption by 36.15 Wh. These findings highlight the model's ability to optimize the trade-off between comfort and energy efficiency. Future work will be conducted with extended control variables and validation duration in actual apartments. |
| Keyword | Reinforcement learning (강화학습), Cooling control (냉방제어), Residential buildings (주거 건물), Thermal preference (열적 선호도), Autonomous control (자율제어) |
| 페이지 | - |
| 논문 파일 | 없음 |
| 게재일시 | 2025-06 |
| DOI | - |
| 학회/저널명 | 2025 대한설비공학회 하계학술발표대회 |
| 년도 | 2025 |
| 추가 문구 | - |
| 등록 일시 | 2025-12-24 15:44:43 |