Abstract
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.
Highlights
IntroductionBackground & SummaryFor many years electricity consumption has only been monitored for billing purposes, only requiring metering devices that provide readings for each billing period
Background & SummaryFor many years electricity consumption has only been monitored for billing purposes, only requiring metering devices that provide readings for each billing period
An aspect highlighted by the authors is missing meta data describing the circumstances of the data recording. We found this to be especially relevant in order to be able to find explanations for behavior and behavior changes
Summary
Background & SummaryFor many years electricity consumption has only been monitored for billing purposes, only requiring metering devices that provide readings for each billing period. In recent years smart-meters have been installed to a greater extent to provide a basis for more complex pricing models as well as a more in-depth consumption analysis. The biggest enabler for research are datasets, and even more significant are publicly available datasets, since only different research teams can evaluate publications and further develop established techniques. This is especially critical as the gathering of high resolution electricity consumption data is accompanied by many ethical questions, especially if not implemented as opt-in[3]
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