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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🔬研究者:The novel MS-GTAN+ model and multi-agent optimization provide benchmarks and architectures for further AI applications in energy system resilience and sustainability.

🏢実務担当者:Campus energy managers can adopt the framework to improve microgrid efficiency, reduce carbon emissions, and ensure critical load supply during disasters.

🏛政策担当者:The paper demonstrates how policy support for smart microgrids and educational digital twins can advance both climate resilience and sustainability education.

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.

• semanticscholar https://doi.org/10.3390/su18020627first seen 2026-06-29 07:52:26