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
Amid growing concerns about health risks, the demand for safer indoor air quality has significantly increased, necessitating advanced control solutions. Previous studies focused on ventilation, air purification control, and the proactive management of indoor air pollutants. However, most of these studies relied on computer simulations, with few applications in real-world environments. In response, this study developed an AI-based integrated control algorithm for indoor PM<sub>2.5</sub> and CO₂ using empirical data. The algorithm incorporates prediction models for indoor PM<sub>2.5</sub> and CO2 based on artificial neural networks, enabling proactive control. It was applied in a living lab to validate its effectiveness in improving indoor air quality and reducing energy consumption. The integrated control algorithm demonstrated an average reduction of 19.69% in indoor PM<sub>2.5</sub> concentrations and 6.59% in CO₂ concentrations compared to the existing rule-based algorithm, while also decreasing energy consumption by 9.42%. Furthermore, the algorithm maintained indoor PM<sub>2.5</sub> and CO₂ concentrations below established criteria for a longer duration, confirming its superior performance. The integrated control algorithm developed in this study effectively achieved both indoor air quality management and energy consumption reduction, contributing to the sustainable development of building environment management and control.
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) : I410-151-25-02-092698577
