Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors
Enhance load forecastability: Optimize data sampling policy by reinforcing user behaviors
复制标题
增强负载可预测性：通过强化用户行为优化数据采样策略
DOI：
10.1016/j.ejor.2021.03.032
复制
发表时间：
2021
影响因子：
6.4
通讯作者：
Weng, Yang
中科院分区：
文献类型：
--
作者：
Xie, Guangrui;Chen, Xi;Weng, Yang
长期以来，负荷预测一直是电力系统可靠规划和运行的一项关键任务。近年来，先进的计量基础设施在工业中激增。这催生了许多基于智能电表频繁测量功率状态的负载预测方法。与此同时，这种新环境中出现的现实约束为实现高负载可预测性带来了挑战和机遇。数据集中器和公用事业之间的传输经常受到的带宽限制就是其中之一，它限制了可以从客户处采样的数据量。缺乏通过与智能电网环境的在线数据交互来自适应用户负载行为的采样率控制策略。在本文中，我们将带宽约束采样率控制问题表述为马尔可夫决策过程（MDP），并提供基于强化学习（RL）的算法来求解最优采样率控制策略的MDP。生成的策略可以实时更新，以适应智能电网中观察到的不稳定负载行为。数值实验表明，所提出的基于强化学习的算法优于竞争算法，并提供卓越的预测性能。
Load forecasting has long been a key task for reliable power systems planning and operation. Over the recent years, advanced metering infrastructure has proliferated in industry. This has given rise to many load forecasting methods based on frequent measurements of power states obtained by smart meters. Meanwhile, real-world constraints arising in this new setting present both challenges and opportunities to achieve high load forecastability. The bandwidth constraints often imposed on the transmission between data concentrators and utilities are one of them, which limit the amount of data that can be sampled from customers. There lacks a sampling-rate control policy that is self-adaptive to users’ load behaviors through online data interaction with the smart grid environment. In this paper, we formulate the bandwidth-constrained sampling-rate control problem as a Markov decision process (MDP) and provide a reinforcement learning (RL)-based algorithm to solve the MDP for an optimal sampling-rate control policy. The resulting policy can be updated in real time to accommodate volatile load behaviors observed in the smart grid. Numerical experiments show that the proposed RL-based algorithm outperforms competing algorithms and delivers superior predictive performance.
智能电表AMI技术综述
DOI：
--
发表时间：
2016
期刊：
2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT)
影响因子：
--
作者：
Saket R Nimbargi;Sagar Mhaisne;Samruddhi Nangare;Manisha Sinha
通讯作者：
Manisha Sinha
DOI：
10.1016/j.ejor.2019.07.061
发表时间：
2020-02
期刊：
Eur. J. Oper. Res.
影响因子：
--
作者：
P. Nystrup;Erik Lindström;P. Pinson;H. Madsen
通讯作者：
P. Nystrup;Erik Lindström;P. Pinson;H. Madsen