Detecting Appliances in Energy Traces

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dc.contributor.authorAlping, Mikael
dc.contributor.authorBergman, Anton
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:24:10Z
dc.date.available2019-07-03T14:24:10Z
dc.date.issued2016
dc.description.abstractThe amount of data gathered in the world is increasing every day. More and more energy data is being gathered from households and smart energy meters. To extend the functionality of these energy traces, Non-Intrusive Load Monitoring (NILM) algorithms can be used. These algorithms use training data in the form of appliancespecific energy trace to label different sections of the aggregated energy trace with activity. In this thesis, we investigate how to create a data set with the goal of using it to investigate NILM algorithms, and to build a platform for future student projects in the area of energy trace data sets. This platform contains suggestions on what methods to use for gathering data, how to store it, and how to analyse it.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/245848
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleDetecting Appliances in Energy Traces
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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