Neural Network-Based Control Model for Occupant-Centric Residential Indoor Air Conditioning
| 주 저자 | 남영도 |
|---|---|
| 공동 저자 | 김세헌, 김태연, 정재원 |
| 소속 | - |
| Abstract | This study proposes a prediction model based on artificial neural networks (ANN) to estimate occupant-defined air conditioner set-point temperatures, thereby enabling personalized indoor thermal control. Existing approaches identified in prior research face limitations due to restricted data collection and inconsistencies between standardized thermal indices—such as the Predicted Mean Vote (PMV)—and actual occupant thermal sensation. To address these challenges, key predictive variables were identified, and the model was trained using data collected from real residential environments. The results demonstrate the feasibility of predicting occupant set-point temperatures, providing a foundation for future implementation of occupant-centric HVAC control in residential buildings. |
| Keyword | - |
| 페이지 | - |
| 논문 파일 | 없음 |
| 게재일시 | 2025-11 |
| DOI | - |
| 학회/저널명 | 2025 ISHVAC |
| 년도 | 2025 |
| 추가 문구 | - |
| 등록 일시 | 2025-12-24 16:59:56 |