Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction-Reference-Cited by-同舟云学术

Probabilistic Physics-Informed Graph Convolutional Network for Active Distribution System Voltage Prediction

Author:

Su Tong¹^ORCID,Zhao Junbo¹^ORCID,Pei Yansong¹^ORCID,Ding Fei²^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA

2. National Renewable Energy Laboratory, Golden, CO, USA

Funder

National Renewable Energy Laboratory

Alliance for Sustainable Energy

LLC

U.S. Department of Energy

U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

Reference10 articles.

1. Simple and deep graph convolutional networks;chen;Proc 37th Int Conf Mach Learn,0

3. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning;gal;Proc 33rd Int Conf Mach Learn,0

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