Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Park, Seonho"'
Autor:
Park, Seonho, Van Hentenryck, Pascal
Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a self-supervised end-to-end primal-dual learning framework for producing
Externí odkaz:
http://arxiv.org/abs/2311.18072
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper fi
Externí odkaz:
http://arxiv.org/abs/2301.08840
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. Independent System Operators (ISOs) also aim at usi
Externí odkaz:
http://arxiv.org/abs/2211.15755
Autor:
Park, Seonho, Van Hentenryck, Pascal
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization under constraints, which is challenging as training must balance opti
Externí odkaz:
http://arxiv.org/abs/2208.09046
Autor:
Barry, Neil, Chatzos, Minas, Chen, Wenbo, Han, Dahye, Huang, Chaofan, Joseph, Roshan, Klamkin, Michael, Park, Seonho, Tanneau, Mathieu, Van Hentenryck, Pascal, Wang, Shangkun, Zhang, Hanyu, Zhao, Haoruo
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. Indeed, the increased stochasticity in load and the volatility of
Externí odkaz:
http://arxiv.org/abs/2204.00950
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring reliable operations of power grids. In a context of growing operational unc
Externí odkaz:
http://arxiv.org/abs/2112.13469
Deep learning-based image retrieval has been emphasized in computer vision. Representation embedding extracted by deep neural networks (DNNs) not only aims at containing semantic information of the image, but also can manage large-scale image retriev
Externí odkaz:
http://arxiv.org/abs/2109.10329
Autor:
Park, Seonho, Pardalos, Panos M.
Estimating the data density is one of the challenging problems in deep learning. In this paper, we present a simple yet effective method for estimating the data density using a deep neural network and the Donsker-Varadhan variational lower bound on t
Externí odkaz:
http://arxiv.org/abs/2104.06612
Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error from variational autoencoder (VAE) via maximizing the evidenc
Externí odkaz:
http://arxiv.org/abs/2005.01889
We focus on minimizing nonconvex finite-sum functions that typically arise in machine learning problems. In an attempt to solve this problem, the adaptive cubic regularized Newton method has shown its strong global convergence guarantees and ability
Externí odkaz:
http://arxiv.org/abs/1906.11417