A neural network approach to high-dimensional optimal switching problems with jumps in energy markets
Optimization and Control
2023-09-19 v2
Abstract
We develop a backward-in-time machine learning algorithm that uses a sequence of neural networks to solve optimal switching problems in energy production, where electricity and fossil fuel prices are subject to stochastic jumps. We then apply this algorithm to a variety of energy scheduling problems, including novel high-dimensional energy production problems. Our experimental results demonstrate that the algorithm performs with accuracy and experiences linear to sub-linear slowdowns as dimension increases, demonstrating the value of the algorithm for solving high-dimensional switching problems.
Keywords
Cite
@article{arxiv.2210.03045,
title = {A neural network approach to high-dimensional optimal switching problems with jumps in energy markets},
author = {Erhan Bayraktar and Asaf Cohen and April Nellis},
journal= {arXiv preprint arXiv:2210.03045},
year = {2023}
}