NILM: Multivariate DNN performance analysis with high frequency features

Camilo Mariño, E. Masquil, Franco Marchesoni, Alicia Fernández, Pablo Massaferro
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引用次数: 3

Abstract

In recent years we have seen deep neural networks (DNNs) appear in almost every signal processing problem. Non Intrusive Load Monitoring (NILM) was not an exception. A detailed evaluation of the supervised deep learning approach can provide powerful insights for future applications on the matter. In this work we improve a state of the art NILM system based on DNN, by including high frequency features and modifying the autoencoders latent space dimension. Moreover, we introduce a novel dataset for evaluating NILM systems. This paper presents a contribution that adds relevant features as a multivariate input to the DNNs, based on high frequency measurements of the power. Furthermore, a thorough evaluation of the generalization capabilities of these models is presented, comparing results from public databases and those acquired locally in Latin America (LATAM), an underrepresented region on the NILM problem. The data and software generated are left of public access.
NILM:具有高频特征的多元深度神经网络性能分析
近年来,我们已经看到深度神经网络(dnn)出现在几乎所有的信号处理问题中。非侵入式负载监控(NILM)也不例外。对监督深度学习方法的详细评估可以为该问题的未来应用提供强有力的见解。在这项工作中,我们通过加入高频特征和修改自编码器的潜在空间维度来改进基于深度神经网络的最先进的NILM系统。此外,我们还引入了一个新的数据集来评估NILM系统。本文提出了一种基于功率的高频测量,将相关特征作为多元输入添加到dnn的贡献。此外,对这些模型的泛化能力进行了全面的评估,比较了公共数据库的结果和在拉丁美洲(LATAM)获得的结果,拉丁美洲(LATAM)是一个在NILM问题上代表性不足的地区。生成的数据和软件不对公众开放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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