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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorThierens, D.
dc.contributor.authorTooren, M.B. van
dc.date.accessioned2020-02-20T19:03:44Z
dc.date.available2020-02-20T19:03:44Z
dc.date.issued2019
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/34853
dc.description.abstractElectrical appliance classification has great potential. It has potential uses in analysis of power usage within households, automation of households, and detection of hazards and electrical decay. The field is well researched, but has yet to see successful mass deployment in the modern world. A new device called the Crownstone may be the solution, as it is capable of intrusive load monitoring and being developed to be distributed into many households. In this research, an easily implementable established method for classification of electrical appliances by intrusive load monitoring is tested on a new, more challenging dataset recorded using Crownstones. An analysis is made of the achievable accuracy, as well as the effects of the noise and larger number of classes. It is found that the method continues to perform surprisingly well under these more demanding conditions, especially with the help of simple preprocessing steps.
dc.description.sponsorshipUtrecht University
dc.language.isoen
dc.titleLarge Scope Device Recognition by Power Usage for Crownstones
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsMachine Learning, Device Recognition, ILM, Feature Extraction
dc.subject.courseuuGame and Media Technology


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