Social Energy System Design Incorporating AI and Lived Experience
| Project/Area Number |
22K18039
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 63040:Environmental impact assessment-related
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| Research Institution | Kyushu University |
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Principal Investigator |
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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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
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| Keywords | machine learning / artificial intelligence / social equity / system design / environmental behavior / energy system design / statistical analysis / survey / Social Equity / Energy System / Behavior / Sustainability |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
This research highlighted the ability for artificial intelligence to enable the prediciton of environmental, economic and social aspects' importance from the viewpoint of society, and how we can utilize this finding to ease both the design of survey instruments, but also to enable energy system design. Particularly in the final academic year of this research support period, I was able to pubish a paper entitled "Enhancing Survey Efficiency and Predictive Ability in Energy System Design through Machine Learning: A Workflow-Based Approach for Improved Outcomes" in the Energies journal. Further work has been submitted to the Journal of Cleaner Production, entitked "Can personal preference and behaviors serve as proxies for energy and sustainability preferences? Contrasting statistical and machine learning approaches", contrasting statistical and machine learning approaches to energy system design and achieving social equity. It is believed that this work not only identifies the potential of machine learning to speed up the process fo understanding stakeholder preferences, but also to apply them to future, desirable and sustainable energy system design.
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Report
(2 results)
Research Products
(7 results)
