Machine Learning in Power Systems: Is It Time to Trust It?
Spyros Chatzivasileiadis, Andreas Venzke, Jochen Stiasny, George S. Misyris
Abstract
We experience the power of machine learning (ML) in our everyday lives—be it picture and speech recognition, customized suggestions by virtual assistants, or just unlocking our phones. Its underlying mathematical principles have been applied since the middle of the last century in what is known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">statistical learning</i> . However, the enormous increase in computational power, even in devices as small as a smartphone, has enabled significant advances and wide adoption of ML in nearly every part of our lives and the scientific world.
Topics & Concepts
Power (physics)Computer sciencePsychologyArtificial intelligencePhysicsQuantum mechanicsModel Reduction and Neural NetworksPower System Optimization and StabilitySmart Grid Security and Resilience