| 研究課題/領域番号 |
23KJ0513
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 |
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| 応募区分 | 国内 |
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| 審査区分 |
小区分23020:建築環境および建築設備関連
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| 研究機関 | 九州大学 |
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研究代表者 |
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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| 研究期間 (年度) |
2023-04-25 – 2025-03-31
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| 研究課題ステータス |
中途終了 (2023年度)
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| 配分額 *注記 |
1,800千円 (直接経費: 1,800千円)
2024年度: 900千円 (直接経費: 900千円)
2023年度: 900千円 (直接経費: 900千円)
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| キーワード | Reinforcement learning / Deep learning / Net zero energy / Renewable energy |
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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
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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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
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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| 今後の研究の推進方策 |
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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