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

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-10-16 DOI:10.1137/22m1527246

Erhan Bayraktar, Asaf Cohen, April Nellis

引用次数: 0

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

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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

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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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.