A synthetic energy dataset for non-intrusive load monitoring in households

• Published in: Scientific data Vol. 7; no. 1; p. 108
• Main Authors: Klemenjak, Christoph, Kovatsch, Christoph, Herold, Manuel, Elmenreich, Wilfried
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.04.2020; Nature Publishing Group
• Subjects: 639/166/987; 639/4077/4073; Climate change; Computer applications; Data Descriptor; Datasets; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; R&D; Research & development; Science; Science (multidisciplinary); Smart grid technology
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0434-6

Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Measurement(s) energy Technology Type(s) computational modeling technique Factor Type(s) appliance •operational state Sample Characteristic - Environment residential building Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11940324