TY - JOUR
T1 - Machine Learning in Power Systems: Is It Time to Trust It?
AU - Chatzivasileiadis, Spyros
AU - Venzke, Andreas
AU - Stiasny, Jochen
AU - Misyris, Georgios
PY - 2022
Y1 - 2022
N2 - 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 statistical learning. 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.
AB - 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 statistical learning. 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.
U2 - 10.1109/MPE.2022.3150810
DO - 10.1109/MPE.2022.3150810
M3 - Journal article
SN - 1540-7977
VL - 20
SP - 32
EP - 41
JO - IEEE Power & Energy Magazine
JF - IEEE Power & Energy Magazine
IS - 3
ER -