Unrolled Spatiotemporal Graph Convolutional Network for Distribution System State Estimation and Forecasting

Author:

Wu Huayi1ORCID,Xu Zhao1ORCID,Wang Minghao2ORCID

Affiliation:

1. Research Institute for Smart Energy (RISE) and the Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, SAR, China

2. Research Institute for Smart Energy (RISE), and Power Electronics research Center, The Hong Kong Polytechnic University, Hong Kong, SAR, China

Funder

Hong Kong Polytechnic University

National Natural Science Foundation of China

Environment and Conservation Fund & Woo Wheelock Green Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Renewable Energy, Sustainability and the Environment

Reference38 articles.

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Prediction Model-Assisted Optimization Scheduling Strategy for Renewable Energy in the Microgrid;Processes;2024-08-23

2. Normalizing Flows-based Probabilistic Learning-Aided Distribution System State Estimation;2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE);2024-08-06

3. Robust Multiarea Distribution System State Estimation Based on Structure-Informed Graphic Network and Multitask Gaussian Process;IEEE Transactions on Industrial Informatics;2024-08

4. Channel spatio-temporal convolutional network for pedestrian trajectory prediction;International Journal of Machine Learning and Cybernetics;2024-06-20

5. A High-Resolution and High-Efficiency Distribution Network State Estimation Framework Based on Micro-PMU Data;2024 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA);2024-05-21

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