Physics-Informed Machine Learning for Power Grid Frequency Modeling
Johannes Kruse, Eike Cramer, Benjamin Schäfer, Dirk Witthaut
Abstract
Toward unlocking the mysteries of power grid dynamics, a physics-inspired machine learning model reveals hidden dependencies and enables precise probabilistic predictions in the power system of continental Europe.
Topics & Concepts
GridProbabilistic logicPhysicsPower (physics)Power gridArtificial intelligenceMachine learningComputer scienceQuantum mechanicsGeometryMathematicsComputational Physics and Python ApplicationsModel Reduction and Neural NetworksEnergy Load and Power Forecasting