The University of Tokyo · 공학
허 저환 교수 연구실은 전력 수요 및 발전 예측, 에너지 정책의 정의적 측면인 에너지 정의, 그리고 재생 가능 에너지 시스템의 최적 운영을 중심으로 한 지능형 에너지 관리 기술을 연구하고 있습니다. 특히 대규모 언어모델과 강화학습을 활용한 시계열 예측 및 자원 배분 최적화 기법을 개발하여 지속 가능한 에너지 시스템의 실현 가능성을 높이고자 합니다. 실증 기반 데이터 분석과 정책 사례 연구를 융합한 다학제적 접근이 특징입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Accurate forecasting of electricity demand and generation is crucial for efficient grid management and sustainable energy planning. While large language models (LLM) have shown promise in various fields, their application to time series forecasting presents challenges, including limited cross-channel information capture and the complexity of prompt design. In this study, we propose a novel framework that combines multiple attention mechanisms with LLM, enabling effective feature extraction from
This research introduces a reinforcement learning optimization framework for renewable energy systems, aimed at advancing Net-Zero Energy Buildings integrated with solar photovoltaic, biomass power generation, and battery storage. To address the challenges posed by mixed action spaces in the deployment of reinforcement learning, an algorithm utilizing a parameterized action space has been employed. This study is capable of managing the operational scheduling of various renewable energy sources w
’Demand-side response’ (DSR), a mechanism through which residential electricity usage adapts based on external cues, has been conceptualized diversely, with numerous experiments showing that DSR frequently results in disparate and inconsistent outcomes for consumers. This diversity in outcomes prompts an examination of implementing such policies, thereby situating them within the discourse of energy justice—a perspective that explores the ethical dimensions of energy systems. However, current re
