首页|Reports from Northeastern University Highlight Recent Findings in Machine Learni ng (Graph-learning-assisted State Estimation Using Sparse Heterogeneous Measurem ents)
Reports from Northeastern University Highlight Recent Findings in Machine Learni ng (Graph-learning-assisted State Estimation Using Sparse Heterogeneous Measurem ents)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting out of Boston, Massachusetts, by New sRx editors, research stated, "Unlike transmission systems, distribution systems historically lack enough measurements, making their realtime monitoring almost impossible." Financial support for this research came from United States Department of Energy (DOE). Our news journalists obtained a quote from the research from Northeastern Univer sity, "Recent deployment of diverse types of devices such as phasor measurement units (PMUs), smart meters, solar inverters and weather information sensors open s up new ways of monitoring these systems, with the assistance of customized mac hine learning (ML) applications. The paper describes a grid-model-informed machi ne learning (ML) tool which integrates heterogeneous data streams and creates sy nchronous measurement snapshots to be used by a hybrid robust state estimator (S E) which provides not only accurate state estimates but also real-time feedback for ML model refinement."
BostonMassachusettsUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesGraph LearningMachine LearningNortheastern University
NETL
NSTL
