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Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

机译：使用人工智能分类器的非侵入式负荷监测：机器学习技术的性能分析

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摘要

In recent years, strategies for load monitoring have been proposed to mitigate power consumption. It has been found, in several reported studies, that as more information is provided for consumers about their electricity consumption, more power energy conservation will occur. In this way, Non-Intrusive Load Monitoring (NILM) has been studied and applied in real-life applications. It consists of detecting and classifying appliances on/off states by measuring electrical signals only at one location of the residential consumer. Several studies have been made using different techniques to improve the accuracy of this strategy. In this paper electromagnetic transients are taking into account and, a performance analysis between cutting-edge artificial classifiers is made. It has been found that 1D convolutional neural networks perform better for this case and electrical current signals are more suitable for NILM, once it carries more features than voltage and power signals.
机译：近年来，已经提出了负载监测的策略来减轻功耗。 在几项研究中发现了它，因为为消费者提供了更多信息，对其电力消费量来说，将会发生更多的功率节约。 以这种方式，已经研究了非侵入式负载监测（NILM）并应用于现实生活中的应用。 它包括通过仅在住宅消费者的一个位置测量电信号来检测和分类设备开/关。 使用不同的技术进行了几项研究，以提高该策略的准确性。 在本文的情况下，考虑到电磁瞬变，并进行了尖端人工分类器之间的性能分析。 已经发现，一旦它具有比电压和功率信号更多的特征，1D卷积神经网络对于这种情况而言更适合于尼尔来表现更好。

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  • 来源
    《Electric power systems research》 |2021年第9期|107347.1-107347.6|共6页
  • 作者

    Monteiro R. V. A.; de Santana J. C. R.; Teixeira R. F. S.; Bretas A. S.; Aguiar R.; Poma C. E. P.;

  • 作者单位

    Univ Fed Mato Grosso Elect Power Syst Operat & Smart Grid Res Lab Cuiaba Mt State Brazil;

    Univ Fed Mato Grosso Elect Power Syst Operat & Smart Grid Res Lab Cuiaba Mt State Brazil;

    Univ Fed Mato Grosso Elect Power Syst Operat & Smart Grid Res Lab Cuiaba Mt State Brazil|Univ Fed Mato Grosso Comp Engn Dept Grp Innovat Real Informat Syst Campus Varzea Grande Cuiaba Mt State Brazil;

    Univ Florida Power Lab Gainesville FL USA;

    Univ Fed Mato Grosso Elect Power Syst Operat & Smart Grid Res Lab Cuiaba Mt State Brazil;

    Univ Fed Mato Grosso Elect Power Syst Operat & Smart Grid Res Lab Cuiaba Mt State Brazil;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Nilm; Electromagnetic transients; Deep learning; Artificial intelligence; Energy management;

    机译：尼尔;电磁瞬变;深入学习;人工智能;能源管理;

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