There is a continuously growing amount of appliances and energy dependent services in households. To date, efforts have mostly focused on energy efficiency, however behavior changes are required for a more sustainable energy consumption. People therefore need to understand their consumption habits to be able to adapt them. Appliance-specific feedback is probably the most efficient way to impact behaviors, since people need to ‘see’ where their electricity goes. Smart meters, currently being extensively rolled out in Europe and in the U.S. are good potential candidates to provide end-users with<p>energy advice. The required functionalities must however be rapidly defined if they are expected to be integrated in the future massive roll out.<p><p>Nonintrusive appliance load monitoring aims to derive appliance-specific information from the aggregate electricity consumption. While techniques have been developed since the 80’s, those mainly address the identification of previously learned appliances, from a database. Building such a database is an intrusive and tedious process which should be avoided. Whereas most recent efforts have focused on unsupervised techniques to disambiguate energy consumption into individual appliances, they usually rely on prior information about measured appliances such as the number of appliances, the number of states in each appliance as well as the power they consume in each state. This information should ideally be learned from the data. This topic will be addressed in the present research.<p><p>This work will present a framework for unsupervised learning for nonintrusive appliance<p>load monitoring. It aims to discover information about appliances of a household solely from its aggregate consumption data, with neither prior information nor user intervention. The learning process can be segmented into five tasks: the detection of on/off switching, the extraction of individual load signatures, the identification of<p>recurrent signatures, the discovery of two-state electrical devices and, finally, the elaboration<p>of appliance models. The first four steps will be addressed in this paper.<p><p>The suite of algorithms proposed in this work allows to discover the set of two-states electrical loads from their aggregated consumption. This, along with the evaluation<p>of their operating sequences, is a prerequisite to learn appliance models from the data. Results show that loads consuming power down to some dozens of watts can be learned from the data. This should encourage future researchers to consider such an unsupervised learning. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished
| Identifer | oai:union.ndltd.org:ulb.ac.be/oai:dipot.ulb.ac.be:2013/209369 |
| Date | 08 November 2013 |
| Creators | Jossen, Quentin |
| Contributors | Maun, Jean Claude, Kinnaert, Michel, Robyns, Benoit, Decaestecker, Christine, Robert, Frédéric |
| Publisher | Universite Libre de Bruxelles, Université libre de Bruxelles, Ecole polytechnique de Bruxelles – Electricien, Bruxelles |
| Source Sets | Université libre de Bruxelles |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis, info:ulb-repo/semantics/doctoralThesis, info:ulb-repo/semantics/openurl/vlink-dissertation |
| Format | 1 v. (vii, 178 p.), 2 full-text file(s): application/pdf | application/pdf |
| Rights | 2 full-text file(s): info:eu-repo/semantics/closedAccess | info:eu-repo/semantics/openAccess |
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