Deep Learning-Based Transmission Line Screening for Unit Commitment
Author:
Affiliation:
1. Rochester Institute of Technology,Department of Electrical and Microelectronics Engineering,Rochester,NY,USA,14623
2. Rochester Institute of Technology,Department of Industrial and Systems Engineering,Rochester,NY,USA,14623
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10252165/10252088/10252766.pdf?arnumber=10252766
Reference18 articles.
1. Security-constrained unit commitment considering locational frequency stability in low-inertia power grids;tuo;IEEE Transactions on Power Systems,2022
2. Accurate prediction of solvent accessibility using neural networks-based regression
3. Impacts of UC formulation tightening on computation of convex hull prices
4. Deep learning-based rolling horizon unit commitment under hybrid uncertainties
5. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies;hochreiter;A Field Guide to Dynamical Recurrent Neural Networks,2001
Cited by 1 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. The Significance of Time Constraints in Unit Commitment Problems;IEEE Access;2024
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