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
In the urban water supply system, a significant proportion of energy consumption is attributed to the water supply pumping station (WSPS). The conventional manual scheduling method employed by water supply enterprises imposes a considerable economic burden. In this paper, we intend to minimize the energy cost of WSPS by dynamically adjusting the combination of pumps and their operational states while considering the pressure difference of the main pipe and switching times of pump group. Achieving this goal is challenging due to the lack of accurate mechanistic models of pumps, uncertainty in environmental parameters, and temporal coupling constraints in the database. Consequently, a WSPS pump scheduling algorithm based on physics‐informed long short‐term memory (PI‐LSTM) surrogate model and multiagent deep deterministic policy gradient (MADDPG) is proposed. The proposed algorithm operates without prior knowledge of an accurate mechanistic model of the pump units. Combining data‐driven with the physical laws of fluid mechanics improves the prediction accuracy of the model compared to traditional data‐based deep learning models, especially when the amount of data is small. Simulation results based on real‐world trajectories show that the proposed algorithm can reduce energy consumption by 13.38% compared with the original scheduling scheme. This study highlights the potential of integrating physics‐informed deep learning and reinforcement learning to optimize energy consumption in urban water supply systems.
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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