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SVM-Based Segmentation of Home Appliance Energy Measurements

  • Generating a more detailed understanding of domestic electricity demand is a major topic for energy suppliers and householders in times of climate change. Over the years there have been many studies on consumption feedback systems to inform householders, disaggregation algorithms for Non-Intrusive-Load-Monitoring (NILM), Real-Time-Pricing (RTP) to promote supply aware behavior through monetary incentives and appliance usage prediction algorithms. While these studies are vital steps towards energy awareness, one of the most fundamental challenges has not yet been tackled: Automated detection of start and stop of usage cycles of household appliances. We argue that most research efforts in this area will benefit from a reliable segmentation method to provide accurate usage information. We propose a SVM-based segmentation method for home appliances such as dishwashers and washing machines. The method is evaluated using manually annotated electricity measurements of five different appliances recorded over two years in multiple households.
Metadaten
Author:Marc WenningerORCiD, Dominik Stecher, Jochen SchmidtORCiD
Parent Title (English):Proceedings 8th IEEE International Conference on Machine Learning and Applications -ICMLA 2019
Document Type:Article (peer reviewed)
Language:English
Publication Year:2019
Tag:Machine Learning
First Page:1666
Last Page:1670
Peer reviewed:Ja
faculties / departments:Fakultät für Informatik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke