首页|Researchers at University of Connecticut Release New Data on Machine Learning (Probabilistic Physics-informed Graph Convolutional Network for Active Distribution System Voltage Prediction)
Researchers at University of Connecticut Release New Data on Machine Learning (Probabilistic Physics-informed Graph Convolutional Network for Active Distribution System Voltage Prediction)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Machine Learning. According to news reportingfrom Storrs, Connecticut, by NewsRx journalists, research stated, “This letter proposes a novel data-drivenprobabilistic physics-informed graph convolutional network (GCN) for active distribution system voltageprediction with PVs and EVs. It leverages both measurements and network topology to accurately andefficiently predict node voltages without the need for an accurate distribution system power flow model.”Funders for this research include United States Department of Energy (DOE), United States Departmentof Energy (DOE).
StorrsConnecticutUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Connecticut
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