Navarro, Víctor M.Barragán, MartaNieto, RubénUreña, JesúsHernández, Álvaro2025-04-012025-04-012025-03-29Navarro, V.M., Barragán, M., Nieto, R. et al. HIFDA - High-Frequency Electrical Voltage and Current Signals from Household Appliances. Sci Data 12, 527 (2025). https://doi.org/10.1038/s41597-025-04859-32052-4463 (online)https://hdl.handle.net/10115/81917This work was supported by the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033 (ALONE project, ref. TED2021-131773B-I00, INDRI project, ref. PID2021-122642OB-C41, and AGINPLACE project, ref. PID2023-146254OB-C43)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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/DatasetNon-Intrusive Load Monitoring (NILM)High-Frequency Electrical SignalsSystem-on-Chip (SoC)Deep Neural NetworksArticlehttps://doi.org/10.1038/s41597-025-04859-3info:eu-repo/semantics/openAccess