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dc.contributor.authorDe, Sagnik
dc.date.accessioned2017-05-19 16:11:31 (GMT)
dc.date.available2017-05-19 16:11:31 (GMT)
dc.date.issued2017-05-19
dc.date.submitted2017
dc.identifier.urihttp://hdl.handle.net/10012/11928
dc.description.abstractSmart meters allow for hourly data collection related to customer's power consumption. However this results in thousands of data points, which hides broader trends in power consumption and makes it difficult for energy suppliers to make decisions regards to a specific customer or to large number of customers. Since data without analysis is useless, various algorithms have been proposed to lower the dimensionality of data, discover trends (eg. regression), study relationships between different types (eg. temperature and power data) of collected data, summarize data (e.g. histogram). This allows for easy consumption by the end user. The smart meter data is very compute intensive to process as there are a large number of houses and each house has the data collected over a few years. To speed up the smart meter data analysis, computer clusters have been used. Ironically, these clusters consume a lot of power. Studies have shown that about 10 % of power is consumed by the computing infrastructure. In this thesis a GPU will be used to perform analysis of smart meter data and it will be compared to a baseline CPU implementation. It will also show that GPUs are not only faster than the CPU, but they are also more power efficient.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectElectricityen
dc.subjectGPUen
dc.subjectData analysisen
dc.subjectPower consumptionen
dc.subjectRegressionen
dc.titleEnergy Efficient Energy Analyticsen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentElectrical and Computer Engineeringen
uws-etd.degree.disciplineElectrical and Computer Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorGolab, Wojciech
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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