关于将预测转化为线性状态空间模型马尔可夫决策过程
On Incorporating Forecasts into Linear State Space Model Markov Decision Processes
摘要：
天气预报信息很可能在未来能源系统的控制中得到越来越多的应用。在本文中，我们介绍了一个增强的状态空间模型制定与线性动力学，其中可以将预测信息，是动态显示的基础状态变量的演变。我们使用的预测演化（MMFE）的鞅模型，以执行必要的一致性属性，必须管理的预测与基础状态的联合演变。该配方还产生联合马尔可夫动态，产生马尔可夫决策过程（MDPs），保持计算易处理。本文是第一个执行MMFE一致性要求内的MDP制定，保持易处理性。
英文摘要：
Weather forecast information will very likely find increasing application in the control of future energy systems. In this paper, we introduce an augmented state space model formulation with linear dynamics, within which one can incorporate forecast information that is dynamically revealed alongside the evolution of the underlying state variable. We use the martingale model for forecast evolution (MMFE) to enforce the necessary consistency properties that must govern the joint evolution of forecasts with the underlying state. The formulation also generates jointly Markovian dynamics that give rise to Markov decision processes (MDPs) that remain computationally tractable. This paper is the first to enforce MMFE consistency requirements within an MDP formulation that preserves tractability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
