Digital Shadowing-Enabled Deep Learning for Carbon-Aware Day-Ahead Scheduling of Integrated Energy Systems Under Forecast Uncertainty
A digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems and demonstrates that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty.