東京大学 · 工学
Zehuan Hu教授の研究室は、電力需要・発電予測の高精度化と再生可能エネルギー統合型スマートビルの最適運用を柱としています。大規模言語モデルを活用した時系列予測フレームワークの構築や、強化学習を用いた分散型エネルギーシステムの最適スケジューリングに関する革新的な研究を進めています。特に、プロンプト設計を不要とする多様な特徴抽出機構の開発や、エネルギー格差の視点を統合した需要側応答の定量的評価にも注力しています。
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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
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