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: This research investigates the development and performance of advanced supply air control strategies aimed at simultaneously enhancing energy efficiency and sustaining acceptable indoor thermal comfort within the service areas of a sports facility. The primary objective is to derive control methodologies that strategically balance these two critical factors while promoting the maximum utilization of the spaces. Method: The research employed computational simulations to evaluate two novel control strategies, such as an adaptive model and a learning model by an artificial neural network algorithm, against a conventional thermostat over a week-long period. Results: The simulation results demonstrated significant energy savings: the adaptive model achieved an approximate 12% decrease in average energy consumption, and the learning model, which utilizes an artificial neural network algorithm, delivered a more substantial 22% reduction, respectively. These savings stem from the models’ capability to minimize system overshooting and mitigate signal fluctuations during operation. Furthermore, both proposed models showed a very high level of effectiveness in maintaining thermal comfort homeostasis compared to the thermostat. The network-based learning model proved particularly advantageous, confirming its improved ability to reduce unnecessary energy consumption via adaptive control and produce robust, effective regression results under varying environmental and operational conditions.
본문·목차
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