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.
피인용 0
초록·키워드
Purpose: Thermal comfort is a critical determinant of occupant health and well-being, and the predicted mean vote (PMV) model has been widely adopted as a standard tool for its assessment. However, PMV consistently shows systematic discrepancies with actual thermal sensation votes (TSV) due to its limited capacity to reflect individual physiological characteristics such as body mass index (BMI). This study aims to address this limitation by developing BMI-based PMV correction coefficients. Method: Using the ASHRAE Global Thermal Comfort Database II, PMV prediction errors were analyzed across BMI categories. The corrected index PMV* is defined as PMV*=PMV+t, where t is a data-driven correction coefficient derived for each combination of BMI category and PMV range. Two models were developed in parallel: a Ridge PMV* model prioritizing interpretability and an XGBoost PMV* model focusing on prediction accuracy, both providing correction coefficient lookup tables. Result: The Ridge PMV* model reduced MAE and RMSE by approximately 8.7% and 8.3% compared to the baseline PMV, while the XGBoost PMV* model achieved superior performance with reductions of approximately 24.0% and 20.2%, respectively. The proposed approach is expected to serve as a practical foundation for occupant-centric thermal environment control, particularly when integrated with non-intrusive image-based BMI estimation technologies.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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