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<p>The increasing demand for efficient energy management in smart grids has led to the development of various Non-Intrusive Load Monitoring (NILM) techniques. These aim to disaggregate energy consumption and classify appliances using data from a single-point smart meter at a household’s grid mains. With the use of machine learning methods, NILM solutions increasingly rely on datasets for training and validation. While datasets like WHITED, BLOND, and UK-DALE provide insights into consumption patterns, they face limitations such as lack of steady-state data, complicated ground-truth or low sampling rates, which hinder detecting low-power appliances. High sampling rates, however, improve classification accuracy and enable identifying these devices. This study introduces the HIgh Frequency household electrical signals DAtaset (HIFDA), a high-frequency dataset capturing steady-state signals from 14 household appliances at 100 kSPS, including the empty grid. Data, collected via a custom System-on-Chip (SoC) device, focuses on active consumption and includes multiple time windows. HIFDA, hosted on Zenodo, ensures its suitability for modern NILM research and other applications.</p>

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HIFDA - High-Frequency Electrical Voltage and Current Signals from Household Appliances

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月29日

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  • Víctor M. Navarro,
  • Marta Barragán,
  • Rubén Nieto,
  • Jesús Ureña,
  • Álvaro Hernández

摘要

The increasing demand for efficient energy management in smart grids has led to the development of various Non-Intrusive Load Monitoring (NILM) techniques. These aim to disaggregate energy consumption and classify appliances using data from a single-point smart meter at a household’s grid mains. With the use of machine learning methods, NILM solutions increasingly rely on datasets for training and validation. While datasets like WHITED, BLOND, and UK-DALE provide insights into consumption patterns, they face limitations such as lack of steady-state data, complicated ground-truth or low sampling rates, which hinder detecting low-power appliances. High sampling rates, however, improve classification accuracy and enable identifying these devices. This study introduces the HIgh Frequency household electrical signals DAtaset (HIFDA), a high-frequency dataset capturing steady-state signals from 14 household appliances at 100 kSPS, including the empty grid. Data, collected via a custom System-on-Chip (SoC) device, focuses on active consumption and includes multiple time windows. HIFDA, hosted on Zenodo, ensures its suitability for modern NILM research and other applications.