강화학습 기반 빌딩의 방별 조명 시스템 조도값 설정 기법
Reinforcement Learning-Based Illuminance Control Method for Building Lighting System
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초록
전 세계적으로 에너지 사용량이 증가함에 따라 지구온난화와 같은 환경문제가 초래되었으며, 이에 각국은 협정‧협약을 통한 에너지 산업의 탈탄소화와 함께 화석 에너지를 신재생에너지로 빠르게 전환 중이다. 발전량이 급변하는 신재생에너지 보급 확대에 따라효율적인 에너지 관리의 필요성이 대두되는 한편, AI 기술이 발전함에 따라 에너지 관리 분야와 결합한 AI 기반 빌딩 에너지 관리시스템(Building Energy Management System, BEMS)의 연구 및 개발이 활발히 이루어지고 있다. 본 논문에서는 강화학습 기법중 Multi-Armed Bandit(MAB) 알고리즘을 활용하여 빌딩 각 방의 조명시스템 전력사용량을 효율적으로 관리함과 동시에 사용자들의 불쾌지수를 최소화할 수 있는 알고리즘을 제안하고, 시뮬레이션을 통해 성능을 검증한다.
Abstract
Various efforts have been made worldwide to respond to environmental problems such as climate change. Research on artificial intelligence (AI)-based energy management has been widely conducted as the most effectiveway to alleviate the climate change problem. In particular, buildings that account for more than 20% of the totalenergy delivered worldwide have been focused as a target for energy management using the building energymanagement system (BEMS). In this paper, we propose a multi-armed bandit (MAB)-based energy managementalgorithm that can efficiently decide the energy consumption level of the lighting system in each room of thebuilding, while minimizing the discomfort levels of occupants of each room.
참고문헌 (10)
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