A deep learning model for gas storage optimization
Computational Finance
2021-03-08 v2
Abstract
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.
Cite
@article{arxiv.2102.01980,
title = {A deep learning model for gas storage optimization},
author = {Nicolas Curin and Michael Kettler and Xi Kleisinger-Yu and Vlatka Komaric and Thomas Krabichler and Josef Teichmann and Hanna Wutte},
journal= {arXiv preprint arXiv:2102.01980},
year = {2021}
}