Yuan Gao

Assistant Professor

Kyushu University

Japan

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About

Yuan Gao is an Assistant Professor at Kyushu University in Japan. His research focuses on optimizing renewable energy systems, employing advanced techniques such as reinforcement learning and deep learning for energy management and forecasting. Recent articles highlight his work on photovoltaic forecasting, energy justice in demand response, and innovative neural network architectures for renewable energy applications.

Articles (17)

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2026

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

Collaborators (7)

Wei-An Chen

University of Tennessee

UNITED STATES

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JUAN SHANG

Assistant Professor

Kyushu University

JAPAN

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LUNING SUN

The Univeristy of Tokyo

JAPAN

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Yasunori Akashi

University of Tokyo

JAPAN

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Shanrui Shi

The Univeristy of Tokyo

JAPAN

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Zheng O'Neill

Texas A&M University

UNITED STATES

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Shohei Miyata

Project Lecturer

The Univeristy of Tokyo

JAPAN

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Yuan Gao

Research Interests

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Articles (17)

Optimizing renewable energy systems with hybrid action space reinforcement learning: A case study on achieving net zero energy in Japan

A novel attention-enhanced LLM approach for accurate power demand and generation forecasting

A novel reinforcement learning method based on generative adversarial network for air conditioning and energy system control in residential buildings

A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting

Robust photovoltaic forecasting under severe data missingness via multi-domain collaboration and covariate interaction

Quantitative analysis of energy justice in demand response: Insights from real residential data in Texas, USA

Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems

Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications

Model-based optimal control strategy for multizone VAV air-conditioning systems for neutralizing room pressure and minimizing fan energy consumption

Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning

Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting

Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan

Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data

Automated fault detection and diagnosis of chiller water plants based on convolutional neural network and knowledge distillation

Interpretable deep learning for hourly solar radiation prediction: A real measured data case study in Tokyo

Collaborators (7)

Wei-An Chen

JUAN SHANG

LUNING SUN

Yasunori Akashi

Shanrui Shi

Zheng O'Neill

Shohei Miyata

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