Bucketized Active Sampling for Learning ACOPF
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Abstract (arXiv)
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their training requires significant data, each instance necessitating the (offline) solving of an OPF. To meet the requirements of market-clearing applications, this paper proposes Bucketized Active Sampling (BAS), a novel active learning framework that aims at training the best possible OPF proxy within a time limit. BAS partitions the input domain into buckets and uses an acquisition function to determine where to sample next. By applying the same partitioning to the validation set, BAS leverages labeled validation samples in the selection of unlabeled samples. BAS also relies on an adaptive learning rate that increases and decreases over time. Experimental results demonstrate the benefits of BAS.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 16 Aug 2022.
Builds on
21 of 43 references.
References · 21
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2015Cyclical Learning Rates for Training Neural Networkscited by 629
- 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
- 2020A Survey of Deep Active Learningcited by 554
- 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 432
- 2019Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methodscited by 94
- 2021Influence Selection for Active Learningcited by 84
- 2021Active Learning for Deep Object Detection via Probabilistic Modelingcited by 79
- 2019Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcited by 77
- 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 65
- 2019Optimal Power Flow Using Graph Neural Networkscited by 63
9 more
- 2020DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problemscited by 47
- 2017PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulationscited by 33
- 2018Pool-Based Sequential Active Learning for Regressioncited by 33
- 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
- 2018Dropout-based Active Learning for Regressioncited by 24
- 2019Machine Learning for AC Optimal Power Flowcited by 24
- 2020Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulationcited by 19
- 2020A Learning-boosted Quasi-Newton Method for AC Optimal Power Flowcited by 8
- 2016Supervised Learning for Optimal Power Flow as a Real-Time Proxycited by 4
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