| Project/Area Number |
23KJ0513
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
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| Section | 国内 |
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| Review Section |
Basic Section 23020:Architectural environment and building equipment-related
|
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| Research Institution | Kyushu University |
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Principal Investigator |
GAO YUAN 九州大学, カーボンニュートラル・エネルギー国際研究所, 助教
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| Project Period (FY) |
2023-04-25 – 2025-03-31
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| Project Status |
Discontinued (Fiscal Year 2023)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2024: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2023: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Reinforcement learning / Deep learning / Net zero energy / Renewable energy |
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| Outline of Research at the Start |
This study uses the deep learning model to improve the control accuracy of the MPC algorithm and achieves the best control effect through the combination of the two prediction models. Through our research, it is believed that the automatic control of the energy system can be realized.
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| Outline of Annual Research Achievements |
The research content mentioned in the application topic has been essentially completed to a high standard. Based on the renewable building energy system that actually exists in Tsukuba, Ibaraki Prefecture, Japan, we explored various possibilities of employing Model Predictive Control and Reinforcement Learning algorithms for its operational control. Deep learning models play a significant role in the prediction of renewable energy systems. The initial concern involves the interpretability of deep learning applications. When employing deep learning predictive models in renewable energy systems, the interpretability of these models becomes crucial. Consequently, system owners need to evaluate the computational process of the model and make informed final decisions.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The applicant has basically completed research on actual renewable energy systems, and has published a total of 12 high-level papers in related fields in Applied Energy, Energy and other journals, all of which the applicant is the first author.
The cooperative company has begun implementation testing of the algorithm.
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| Strategy for Future Research Activity |
The next aspect concerns the practical deployment and application of fault diagnosis models within the system. Our study, using data from air conditioning systems as an example, aims to reduce the parameter count of the proposed model without compromising the accuracy of fault diagnosis. This approach is intended to enhance computational speed in engineering applications.
In summary, the applicant has successfully achieved the established objectives set forth in the application topic and has also produced numerous scholarly papers.
In the future, we will further improve the efficiency of the algorithm based on previous research and conduct more actual case analysis and implementation.
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