能源市场中具有跳跃的高维最优切换问题的神经网络方法

IF 1.4 4区经济学 Q3 BUSINESS, FINANCE

SIAM Journal on Financial Mathematics Pub Date : 2023-10-16 DOI:10.1137/22m1527246

Erhan Bayraktar, Asaf Cohen, April Nellis

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引用次数: 0

摘要

我们开发了一种向后时间机器学习算法，该算法使用一系列神经网络来解决能源生产中的最优切换问题，其中电力和化石燃料价格受到随机跳跃的影响。然后，我们将该算法应用于各种能源调度问题，包括新的高维能源生产问题。实验结果表明，该算法具有较高的准确性，并且随着维数的增加而经历线性到次线性的减速，证明了该算法在解决高维切换问题方面的价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Neural Network Approach to High-Dimensional Optimal Switching Problems with Jumps in Energy Markets

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 sublinear slowdowns as dimension increases, demonstrating the value of the algorithm for solving high-dimensional switching problems.

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来源期刊

SIAM Journal on Financial Mathematics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

CiteScore

2.30

自引率

10.00%

发文量

期刊介绍： SIAM Journal on Financial Mathematics (SIFIN) addresses theoretical developments in financial mathematics as well as breakthroughs in the computational challenges they encompass. The journal provides a common platform for scholars interested in the mathematical theory of finance as well as practitioners interested in rigorous treatments of the scientific computational issues related to implementation. On the theoretical side, the journal publishes articles with demonstrable mathematical developments motivated by models of modern finance. On the computational side, it publishes articles introducing new methods and algorithms representing significant (as opposed to incremental) improvements on the existing state of affairs of modern numerical implementations of applied financial mathematics.