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 examines the effectiveness of an optimized, occupant-centric supply air thermal control strategy engineered to enhance the operational performance of a management office. The research focuses on establishing a control paradigm capable of resolving the classic trade-off between minimizing building energy consumption and maintaining desired indoor thermal comfort levels. Method: A straightforward thermal energy balance of the facility was formulated to quantify dynamic temperature variations over time. The thermal comfort index was implemented as the core ergonomic metric to track indoor comfort deviations. An adaptive control loop featuring step-by-step air supply adjustment logic was developed and utilized to train an artificial neural network structured. The combined the adaptive control loop and the network algorithm were subsequently integrated into a unified simulation block framework. Results: The adaptive control model demonstrated high statistical accuracy for both mass flow rate and supply air temperature. Multi-seasonal simulation results revealed that the adaptive model successfully mitigates the severe temperature fluctuations and overshooting typical of traditional dead-band thermostats. During peak summer demands, the proposed model achieved about 20% increase in energy efficiency alongside a massive 46% improvement in thermal comfort tracking precision. Annually, the framework sustained about 6% reduction in overall energy footprint and about 38% increase in comfort homeostasis, respectively.
본문·목차
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오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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