Physics-Informed Machine Learning for Electrical Energy Management

Machine learning can support energy management by enabling accurate forecasting, optimal control, and real-time decision-making in complex energy systems. Physics-Informed Neural Networks (PINNs) integrate physical laws and engineering constraints into learning models, improving accuracy, interpretability, and data efficiency. In electrical energy systems, these methods can address power flow analysis, grid stability, fault diagnosis, and the optimal operation of electrical networks. They can also support renewable energy integration, energy storage management, demand-side flexibility, and smart grid optimization. Combining physics-informed learning with optimization techniques offers promising solutions for efficient, reliable, and sustainable electrical energy management.