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DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany
dataset
posted on 2021-01-28, 13:36 authored by Marc WenningerMarc Wenninger, Jochen Schmidt, Andreas MaierThe 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.
For further details and usage instructions consult the README.md
Python Dataset Loader: https://github.com/DEDDIAG/deddiag-loader
Monitoring System: https://deddiag.github.io
Funding
KMU-innovativ-Verbundprojekt Klimaschutz: Increasing load shifting potential through self-learning home automation algorithms with flexible optimization criteria (self-learning LV), subproject 2, extension and configuration of home automation using self-learning algorithms
Federal Ministry of Education and Research
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Keywords
NILMhousehold energyHousehold electrical appliancesAppliance-by-appliance Energy ConsumptionEnergy DisaggregationEvent DetectionDomestic energydomestic electrical applianceDomestic electricity demandelectricity demand patternselectricity demandhousehold electricity cost optimizationGermanyKnowledge Representation and Machine LearningElectrical and Electronic Engineering not elsewhere classified
CC BY 4.0