Review reveals artificial intelligence frameworks for multi-energy coordination and forecasting, indicating new pathways toward trusted, secure, and decarbonized energy systems.
Key Points
To outline how artificial intelligence paradigms can drive the transition toward carbon-neutral, distributed, and multi-energy coupled systems.
Synthesized AI methodologies including physics-informed learning, reinforcement learning, digital twins, and generative modeling across energy lifecycles.
Evaluated the integration of physical mechanisms, uncertainty quantification, safety constraints, and explainability into energy dispatch and carbon monitoring.
Identified critical capabilities of advanced AI in improving renewable energy forecasting, multi-energy (electricity, heat, gas, and hydrogen) dispatch, and emission accounting.
Highlighted the necessary paradigm shift from black-box predictions to verifiable, physics-constrained decision systems while noting remaining challenges in data privacy and cybersecurity.