2022 Fiscal Year Final Research Report
Automatic control logic utilizing sparse modeling for natural ventilation operation
| Project/Area Number |
19K04741
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 23020:Architectural environment and building equipment-related
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| Research Institution | Meiji University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
Srisamranrungruang Thanyalak 明治大学, 研究・知財戦略機構(生田), 研究推進員(ポスト・ドクター) (40837267)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | 自然換気 / 機械学習 |
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| Outline of Final Research Achievements |
The objective of this research is to develop an automatic control logic for natural ventilation openings that eliminates the dissatisfaction of workers and operators while guaranteeing the energy-saving effects of natural ventilation expected by designers. The developed automatic control logic is based on a policy of improving the indoor environment during natural ventilation operation by utilizing environmental prediction technology based on physical models and machine learning.
The effectiveness of the trial model, which was developed throughout the research period, was confirmed through case studies. In the case study, by simplifying the setting task to select the preferred natural ventilation window opening pattern of full-open and half-open throughout the day, we realized a learning model that can function redundantly even in situations where data size is small, which is the issue in this study.
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| Free Research Field |
建築環境工学
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| Academic Significance and Societal Importance of the Research Achievements |
特徴量変数の数と種類を設計変数としたパラメトリックスタディにおいては、特徴量変数を減少させることにより、モデルの冗長性を向上させるものの、運用の継続におけるデータ量の増加による予測精度向上の効果が小さくなる傾向が示された。また、その入力データの環境工学的知見に基づく加工の有無が、モデルの冗長性に影響をあたることも明らかにした。この建築環境工学の知見の導入に関しては、事前に対象建物を再現した仮想モデルにおけるシミュレーションを通し、建築物の特徴と気象の関係を明らかにする事前作業が有用であり、その方法論に関しては、建築環境工学が対象とする他の課題にも適用可能である点が本研究の学術的な意義となる。
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