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Capturing High-Frequency Harmonic Signatures for NILM: Building a Dataset for Load Disaggregation.

Farid Dinar1, Sébastien Paris1, Éric Busvelle1,2

  • 1Laboratoire d'Informatique et des Systèmes (LIS), Unité Mixte de Recherche, Centre National de la Recherche Scientifique (UMR, CNRS) 7020, Université de Toulon, Aix Marseille Université, 83130 La Garde, France.

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Summary
This summary is machine-generated.

This study introduces a new, cost-effective system for high-frequency energy monitoring to improve Non-Intrusive Load Monitoring (NILM). Machine learning with high-frequency data significantly enhances appliance disaggregation accuracy.

Keywords:
electrical measurements based on high-frequency signalsembedded systemsenergy disaggregationharmonic analysismachine learningnon-intrusive load monitoring (NILM)smart energy systems

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Traditional Non-Intrusive Load Monitoring (NILM) struggles with low-frequency data, limiting appliance disaggregation accuracy.
  • High-frequency electrical data offers richer signatures, including harmonic orders, for improved NILM performance.
  • A lack of comprehensive, accessible public datasets has hindered advanced NILM research.

Purpose of the Study:

  • To develop a scalable, cost-effective energy monitoring system for generating detailed NILM datasets.
  • To create a reproducible and accessible dataset of aggregate and individual appliance measurements.
  • To validate the effectiveness of high-frequency features in improving NILM disaggregation accuracy using machine learning.

Main Methods:

  • Designed and implemented a novel energy monitoring system for high-frequency data acquisition.
  • Collected aggregate and individual appliance electrical data to form a new NILM dataset.
  • Applied machine learning techniques to analyze high-frequency electrical features for appliance disaggregation.

Main Results:

  • The developed system provides detailed, high-frequency electrical measurements.
  • The new dataset enables reproducible NILM validation and research.
  • Machine learning models utilizing high-frequency features demonstrated superior disaggregation accuracy compared to traditional methods.

Conclusions:

  • High-frequency data acquisition systems are crucial for advancing NILM research.
  • The developed dataset and methodology address a critical gap in energy disaggregation.
  • This work paves the way for improved real-time energy disaggregation and smart energy management applications.