1718 2021 eng 15 176 8 article 0 -- -- -- Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of. Scientific Data https://doi.org/10.1038/s41597-021-00963-2 Ja LV-selbstlernend Ja Erhöhung des Lastverschiebungspotenzials durch selbstlernende Home Automation Algorithmen mit flexiblen Optimierungskriterien BMBF 01LY1506 publish false true Marc Wenninger Andreas Maier Jochen Schmidt eng uncontrolled Machine Learning Zeitschriften, fortlaufende Sammelwerke Fakultät für Informatik Technische Hochschule Rosenheim