论文标题
使用高频数据和神经网络的端到端尼尔姆系统
End-to-end NILM System Using High Frequency Data and Neural Networks
论文作者
论文摘要
提高能源效率是抵抗气候变化的必要条件。非侵入性负载监控（NILM）系统提供了有关电力公司或最终用户可以使用的家庭消费的重要信息。在这项工作中，提出了端到端尼尔姆系统的实现，其中包括一种基于自定义的高频计和基于神经网络的算法。本文提出了一种新颖的方式，将高频信息包括作为神经网络模型的输入的多元时间序列，包括精心选择的功能。此外，它提供了对概括误差的详细评估，并表明该类别的模型很好地推广到了训练设备的新实例。收集了两个乌拉圭房屋中测量的评估数据库，并提供了一般无监督方法的讨论。
Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is provided.
