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Found 10 papers, 2 papers with code
Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing near-optimal solutions to large-scale SCOPF problems in milliseconds.
Compact Optimization Learning for AC Optimal Power Flow
This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA).
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors.
Self-Supervised Primal-Dual Learning for Constrained Optimization
This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference.
Risk-Aware Control and Optimization for High-Renewable Power Grids
The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.
Uncertainty Quantification
Vocal Bursts Intensity Prediction
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i. e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions.
Homography augumented momentum constrastive learning for SAR image retrieval
Deep learning-based image retrieval has been emphasized in computer vision.
Deep Data Density Estimation through Donsker-Varadhan Representation
Estimating the data density is one of the challenging problems in deep learning.
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable.
Combining Stochastic Adaptive Cubic Regularization with Negative Curvature for Nonconvex Optimization
In this paper, we suggest an algorithm combining negative curvature with the adaptive cubic regularized Newton method to update even at unsuccessful iterations.
