Academic Journal
Sustainable Energy Transitions in Smart Campuses: An AI-Driven Framework Integrating Microgrid Optimization, Disaster Resilience, and Educational Empowerment for Sustainable Development.
| Τίτλος: | Sustainable Energy Transitions in Smart Campuses: An AI-Driven Framework Integrating Microgrid Optimization, Disaster Resilience, and Educational Empowerment for Sustainable Development. |
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| Συγγραφείς: | Li, Zhanyi, Liu, Zhanhong, Zhou, Chengping, Su, Qing, Xie, Guobo |
| Πηγή: | Sustainability (2071-1050); Jan2026, Vol. 18 Issue 2, p627, 21p |
| Περίληψη: | Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students' understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. [ABSTRACT FROM AUTHOR] |
| Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Sustainable Energy Transitions in Smart Campuses: An AI-Driven Framework Integrating Microgrid Optimization, Disaster Resilience, and Educational Empowerment for Sustainable Development. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Zhanyi%22">Li, Zhanyi</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhanhong%22">Liu, Zhanhong</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Chengping%22">Zhou, Chengping</searchLink><br /><searchLink fieldCode="AR" term="%22Su%2C+Qing%22">Su, Qing</searchLink><br /><searchLink fieldCode="AR" term="%22Xie%2C+Guobo%22">Xie, Guobo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sustainability (2071-1050); Jan2026, Vol. 18 Issue 2, p627, 21p – Name: Abstract Label: Abstract Group: Ab Data: Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students' understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sustainability (2071-1050) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/su18020627 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 627 Titles: – TitleFull: Sustainable Energy Transitions in Smart Campuses: An AI-Driven Framework Integrating Microgrid Optimization, Disaster Resilience, and Educational Empowerment for Sustainable Development. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Zhanyi – PersonEntity: Name: NameFull: Liu, Zhanhong – PersonEntity: Name: NameFull: Zhou, Chengping – PersonEntity: Name: NameFull: Su, Qing – PersonEntity: Name: NameFull: Xie, Guobo IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20711050 Numbering: – Type: volume Value: 18 – Type: issue Value: 2 Titles: – TitleFull: Sustainability (2071-1050) Type: main |
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