Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least $25\%$ while transmitting $80\%$ less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.
翻译:共享网络时间序列数据(如蜂窝或电荷负荷模式)的预测可以改进从交通调度到发电等独立控制应用程序。通常,预测的设计并不了解下游控制员的任务目标,因此只能优化平均预测错误。然而,这种任务-不可知性表示往往太大,无法在通信网络中流出,并不强调合作控制所需的显著时间特征。本文提供了一个解决方案,以学习与模块控制器的任务目标共同设计的简明、高压预测。我们用真实手机、互联网和电荷数据进行的模拟显示,我们可以将模型预测控制器的性能至少提高25美元,同时传送的数据比竞争性方法少80美元。此外,我们为经典线性二次调控管(LQR)的网络变式提供了理论压缩结果。