| 研究課題/領域番号 |
22K18039
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分63040:環境影響評価関連
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| 研究機関 | 九州大学 |
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研究代表者 |
Chapman Andrew 九州大学, カーボンニュートラル・エネルギー国際研究所, 准教授 (60795293)
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| 研究期間 (年度) |
2022-04-01 – 2024-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2023年度: 1,170千円 (直接経費: 900千円、間接経費: 270千円)
2022年度: 3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
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| キーワード | environmental behavior / social equity / energy system design / machine learning / statistical analysis / survey / Social Equity / Energy System / Behavior / Sustainability |
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| 研究開始時の研究の概要 |
This research utilizes big data, national surveys, consumption (behavioral) data and direct stakeholder input to establish a machine learning model and visualization interface to detail stakeholder's social aspects and preferences toward an equitable and desirable future energy system.
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| 研究実績の概要 |
Many research achievements have been made under this research plan. Firstly, the theoretical underpinnings of machine learning and prediction of environmental and social equity preferences of stakeholders via existing data sets (previously undertaken surveys) was established and reported in research currently under review at Applied Energy. The manuscript details multiple machine learning algorithmic approaches to energy system design preferences and the effects of imcreasing training data toward accuracy rates. Based on these findings, a national survey was designed and deployed, specifically to achieve a low-burden, high yield predictive data set which will be leveraged in the coming year. Currently this data is being used to underpin efforts toward a prediciton and visualization tool.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
Underpinning theoretical work is complete on schedule, allowing for the leverage of findings and obtained data toward teh development of prediction and visualization tools. All envisaged activities for AY2022 were completed and it is expected that work on the tools can be proceeded smootly in AY2023.
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| 今後の研究の推進方策 |
This year's activities will be separated into 3 phases - first, survey data analysis will be undertaken to establish linkages between behaviors and preferences. From these analyses a machine learning and prediciotn model will be established and subsequently tested at multiple workshops. Finally, the findings of these workshops and machine learning activities will inform the development of a publicly available prediciton and visualization tool and supporting academic publications.
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