인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Purpose: This study aimed to develop a CO₂ prediction model to enhance indoor air quality (IAQ) management by incorporating real-time occupant-specific characteristics such as activity levels (MET), gender, and BMI. Traditional models typically rely on basic variables like occupant count, often ignoring individual factors that significantly affect CO₂ emissions. By including these variables, the model seeks to improve predictive accuracy and optimize ventilation control systems for enhanced energy efficiency and IAQ management. Method: Two predictive models were developed using DNN and GRU machine learning algorithms. One model utilized basic number of occupants and environmental (outdoor CO₂ , indoor CO₂ , ventilation system flowrate) data, while the enhanced model incorporated MET, gender, and BMI. Both models were trained on mock-up data collected from a controlled environment, including CO₂ concentration, ventilation flow rates, and occupant information. Their performance was evaluated using MAE, CvRMSE, and R². Result: The enhanced model, integrating occupant-specific variables, demonstrated significant improvements in prediction accuracy compared to the traditional model, with MAE and CvRMSE values improving by 3.93% and 6.92%, respectively. These results highlight the importance of detailed occupant data for real-time IAQ management and the potential for greater efficiency in ventilation control and energy savings in sustainable buildings.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-25-02-091046319
