论文标题
部分可观测时空混沌系统的无模型预测
A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting
论文作者
论文摘要
预测间隔（PI）是量化不确定性的有效工具，通常是下游强大优化的输入。传统方法的重点是提高统计分数的PI质量，并假设质量的提高将导致更高的电力系统操作价值。但是，这样的假设在实践中不能总是存在。在本文中，我们提出了一种以价值为导向的PI预测方法，该方法旨在降低下游操作的运营成本。为此，需要在强大的优化中向操作成本的指导发布PI，这是在此处的上下文强盗框架内解决的。具体而言，代理用于选择最佳分位数比例，而环境则将操作成本显示为代理商的奖励。因此，代理可以学习分位数选择的政策，以最大程度地降低运营成本。关于虚拟发电厂的两次计算操作的数值研究验证了拟议方法在操作价值方面的优越性。在广泛的风能渗透中，这一点尤其明显。
Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cannot always hold in practice. In this paper, we propose a value-oriented PI forecasting approach, which aims at reducing operational costs in downstream operations. For that, it is required to issue PIs with the guidance of operational costs in robust optimization, which is addressed within the contextual bandit framework here. Concretely, the agent is used to select the optimal quantile proportion, while the environment reveals the costs in operations as rewards to the agent. As such, the agent can learn the policy of quantile proportion selection for minimizing the operational cost. The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value. And it is especially evident in the context of extensive penetration of wind power.
