论文标题
部分可观测时空混沌系统的无模型预测
Collaborative Load Management in Smart Home Area Network
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
An efficient Home Area Network (HAN) acts as a base of an Advanced Metering Infrastructure (AMI). A HAN not only facilitates AMI with efficient real-time monitoring of the electricity consumption but also manages the load profile of the whole system. However, the existing works on implementing HAN are mostly centralized and suffer from well-known problems. In this work, we propose an IoT-based efficient decentralized strategy using synchronous transmission to practically realize HAN. An inter-device coordination strategy is proposed to minimize the peak load as well as reduce the sudden changes in the overall system without compromising the users requirements. Through experiments over IoT-testbeds, we demonstrate that the proposed strategy can reduce the peak load upto 50% and reduce the load variations upto 58% for even a high and random rate of requests for execution of power-hungry house appliances.
