论文标题
一种神经网络方法，用于高维最佳切换问题，并在能源市场中跳跃
A neural network approach to high-dimensional optimal switching problems with jumps in energy markets
论文作者
论文摘要
我们开发了一种落后的机器学习算法，该算法使用一系列神经网络来解决能源生产中的最佳切换问题，在这种情况下，电力和化石燃料价格可能会受到随机上升的影响。然后，我们将此算法应用于各种能源调度问题，包括新的高维能量生产问题。我们的实验结果表明，随着尺寸的增加，该算法的准确性和经验线性至亚线性放缓，这表明了算法用于解决高维开关问题的算法。
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.
