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
The rapid growth of the global population and urbanization has significantly reduced the availability of arable land, threatening food supply chains. In response, urban agriculture has emerged as a viable solution to address future food security challenges while promoting sustainability in urban settings. Among various urban agriculture models, building-integrated rooftop greenhouses (BiRTGs) present a promising approach by utilizing underused building spaces and enhancing energy efficiency through dynamic energy exchanges between buildings and greenhouses. This study developed a predictive model to optimize the operational control of BiRTGs, focusing on how greenhouse control variables impact the heating and cooling loads of both the building and the greenhouse. An artificial neural network (ANN) was used to simulate the complex thermal interactions between the two structures. The ANN models were trained with simulation data from a validated TRNSYS model, and their predictive accuracy was assessed using performance metrics outlined in ASHRAE Guideline 14. The results indicated that the ANN models achieved high accuracy in predicting the heating and cooling loads of BiRTGs, establishing a strong foundation for the development of real-time optimization algorithms for these systems.
본문·목차
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