论文标题
交易风能的在线决策
Online Decision Making for Trading Wind Energy
论文作者
论文摘要
我们在在线学习和优化框架内提出并开发了一种用于电力市场中风能交易的新算法。特别是，我们结合了梯度下降算法的组件自适应变体与功能驱动的新闻册模型的最新进展。这导致了一种在线产品的方法，能够利用数据丰富的环境，同时适应能源发电和发电市场的非机构特征，也具有最小的计算负担。根据几个数值实验，对我们的方法的性能进行了分析，既显示了对非平稳性不确定参数的更好适应性，又显示出大量的经济增长。
We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning and optimization framework. In particular, we combine a component-wise adaptive variant of the gradient descent algorithm with recent advances in the feature-driven newsvendor model. This results in an online offering approach capable of leveraging data-rich environments, while adapting to the nonstationary characteristics of energy generation and electricity markets, also with a minimal computational burden. The performance of our approach is analyzed based on several numerical experiments, showing both better adaptability to nonstationary uncertain parameters and significant economic gains.
