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

Abstract

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.