Bucketized Active Sampling for learning ACOPF

Klamkin, Michael; Tanneau, Mathieu; Mak, Terrence W. K.; Van Hentenryck, Pascal

doi:10.1016/j.epsr.2024.110697

Title: Bucketized Active Sampling for learning ACOPF

Journal Article · 01 October 2024 · Electric Power Systems Research

DOI: https://doi.org/10.1016/j.epsr.2024.110697 · OSTI ID:2382826

;

; Mak, Terrence W. K.; Van Hentenryck, Pascal

Not Available

View Accepted Manuscript (Publisher)

Cite

Export

Save

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001136

OSTI ID:: 2382826

Journal Information:: Electric Power Systems Research, Journal Name: Electric Power Systems Research Journal Issue: C Vol. 235; ISSN 0378-7796

Publisher:: ElsevierCopyright Statement

Country of Publication:: Switzerland

Language:: English

References (16)

Queries and concept learning Angluin, Dana Machine Learning, Vol. 2, Issue 4 https://doi.org/10.1007/BF00116828	journal	April 1988
Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey Kumar, Punit; Gupta, Atul Journal of Computer Science and Technology, Vol. 35, Issue 4 https://doi.org/10.1007/s11390-020-9487-4	journal	July 2020
Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization Biegler, L. T.; Zavala, V. M. Computers & Chemical Engineering, Vol. 33, Issue 3 https://doi.org/10.1016/j.compchemeng.2008.08.006	journal	March 2009
Learning optimization proxies for large-scale Security-Constrained Economic Dispatch Chen, Wenbo; Park, Seonho; Tanneau, Mathieu Electric Power Systems Research, Vol. 213 https://doi.org/10.1016/j.epsr.2022.108566	journal	December 2022
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Influence Selection for Active Learning Liu, Zhuoming; Ding, Hao; Zhong, Huaping 2021 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV48922.2021.00914	conference	October 2021
Active Learning for Deep Object Detection via Probabilistic Modeling Choi, Jiwoong; Elezi, Ismail; Lee, Hyuk-Jae 2021 IEEE/CVF International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV48922.2021.01010	conference	October 2021
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems Pan, Xiang; Chen, Minghua; Zhao, Tianyu IEEE Systems Journal, Vol. 17, Issue 1 https://doi.org/10.1109/JSYST.2022.3201041	journal	March 2023
A decomposition and coordination approach for large-scale security constrained unit commitment problems with combined cycle units Sun, Xiaorong; Luh, Peter B.; Bragin, Mikhail A. 2017 IEEE Power & Energy Society General Meeting https://doi.org/10.1109/PESGM.2017.8274098	conference	July 2017
Pool-Based Sequential Active Learning for Regression Wu, Dongrui IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 5 https://doi.org/10.1109/TNNLS.2018.2868649	journal	May 2019
The QC Relaxation: A Theoretical and Computational Study on Optimal Power Flow Coffrin, Carleton; Hijazi, Hassan L.; Van Hentenryck, Pascal IEEE Transactions on Power Systems, Vol. 31, Issue 4 https://doi.org/10.1109/TPWRS.2015.2463111	journal	July 2016
Real-Time Optimal Power Flow: A Lagrangian Based Deep Reinforcement Learning Approach Yan, Ziming; Xu, Yan IEEE Transactions on Power Systems, Vol. 35, Issue 4 https://doi.org/10.1109/TPWRS.2020.2987292	journal	July 2020
Real-Time Optimal Power Flow Tang, Yujie; Dvijotham, Krishnamurthy; Low, Steven IEEE Transactions on Smart Grid, Vol. 8, Issue 6 https://doi.org/10.1109/TSG.2017.2704922	journal	November 2017
A Survey of Deep Active Learning Ren, Pengzhen; Xiao, Yun; Chang, Xiaojun ACM Computing Surveys, Vol. 54, Issue 9 https://doi.org/10.1145/3472291	journal	October 2021
MA57---a code for the solution of sparse symmetric definite and indefinite systems Duff, Iain S. ACM Transactions on Mathematical Software, Vol. 30, Issue 2 https://doi.org/10.1145/992200.992202	journal	June 2004
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods Fioretto, Ferdinando; Mak, Terrence W. K.; Van Hentenryck, Pascal Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, Issue 01 https://doi.org/10.1609/aaai.v34i01.5403	journal	April 2020

Similar Records

Learning optimization proxies for large-scale Security-Constrained Economic Dispatch

Journal Article · 2022 · Electric Power Systems Research · OSTI ID:1880377

Chen, Wenbo; Park, Seonho; Tanneau, Mathieu; +1 more

The how-to of stormwater sampling--Not just a drop in the bucket

Journal Article · 1993 · Public Works; (United States) · OSTI ID:5581491

Sinclair, C T; Ray, J A

Proving global optimality of ACOPF solutions

Journal Article · 2020 · Electric Power Systems Research · OSTI ID:2280030

Gopinath, S.; Hijazi, H. L.; Weisser, T.; +4 more

Title: Bucketized Active Sampling for learning ACOPF

Citation Formats

References (16)

Similar Records

Related Subjects