Author
Listed:
- Zhanyi Li
(School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China)
- Zhanhong Liu
(School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)
- Chengping Zhou
(School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)
- Qing Su
(School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)
- Guobo Xie
(School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China)
Abstract
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.
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