The University of Tokyo · Engineering
Professor Zehuan Hu's research lab specializes in smart energy systems, with a focus on renewable energy integration, electricity demand and generation forecasting, and energy justice in residential energy management. The lab develops advanced AI-driven frameworks—such as LLM-enhanced attention mechanisms and reinforcement learning algorithms—to optimize energy scheduling and improve grid efficiency. It emphasizes practical applications using real-world data from diverse energy systems, particularly in Japan and Texas, USA, to address challenges in net-zero energy buildings and equitable energy policies. The lab bridges cutting-edge machine learning with sustainable energy solutions, aiming for both technical innovation and social equity in energy systems.
Figures are computed from collected data and may differ slightly.
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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