Marc Wenninger, Andreas Maier & Jochen Schmidt have recently released DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The data set contains recordings from 15 homes over a period of up to 3.5 years, in which 50 appliances have been recorded at a frequency of 1 Hz. The data set focuses on appliances 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. The authors have also released source code of the data collection system, as well as a python command line tool for loading the data.
My name is Oliver Parson, and I'm currently employed as a Senior Data Scientist at Bulb. I'm interested in investigating the ways in which machine learning can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NILM) or energy disaggregation.
Tuesday, 20 July 2021
Sunday, 7 February 2021
NILM 2020 presentation videos available on Youtube
The NILM 2020 conference was held online in November last year, and featured 26 presentations on recent progress in the field. The individual talks are now available on Youtube:
https://www.youtube.com/playlist?list=PLhs3Zf0KIDKnzUNwf8sXeN7QjsLTmlvnP
An introduction to the conference can be found in the following video:
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