Shohei Miyata

Project Lecturer

The Univeristy of Tokyo

Japan

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About

Shohei Miyata is a Project Lecturer at The University of Tokyo, Japan. His research areas focus on optimal control strategies for HVAC systems, enhancing energy efficiency in smart buildings, and automated fault detection in building infrastructure. Recent publications include studies on multi-agent control strategies, demand-controlled ventilation, and the application of deep learning in HVAC systems.

Articles (10)

Year:

2025

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2025

Year:

2024

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2023

Year:

2023

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2023

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Collaborators (7)

Jin Wen

Professor

Drexel University

UNITED STATES

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Anna Marszal-Pomianowska

Aalborg University

DENMARK

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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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Seungjae Lee

Assistant Professor

University of Toronto

CANADA

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Zheng O'Neill

Texas A&M University

UNITED STATES

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Yuan Gao

Assistant Professor

Kyushu University

JAPAN

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Shohei Miyata

Research Interests

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About

Articles (10)

A hybrid multi-agent distributed optimal control strategy of multizone VAV systems for edge computing in smart buildings

Event-driven model-based optimal demand-controlled ventilation for multizone VAV systems: Enhancing energy efficiency and indoor environmental quality

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

A review of data-driven fault detection and diagnostics for building HVAC systems

Energy saving and indoor temperature control for an office building using tube-based robust model predictive control

How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method

Improving training efficiency for scalable automated fault detection and diagnosis in chilled water plants by transfer learning

Model predictive control of a building renewable energy system based on a long short-term hybrid model

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

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Collaborators (7)

Jin Wen

Anna Marszal-Pomianowska

Yasunori Akashi

Shanrui Shi

Seungjae Lee

Zheng O'Neill

Yuan Gao

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