Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77297
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dc.contributor.authorDing, Hongyuan
dc.date.accessioned2019-05-24T04:15:21Z
dc.date.available2019-05-24T04:15:21Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77297
dc.description.abstractNon-intrusive load monitoring (NILM) can provide a large amount of information users, power utilities for demand response and user-side management. This dissertation surveys NILM methodology, and outlines its basic principle framework and the applications in the first four chapters. In Chapter 5, an ARIMA-Neural Network model is proposed to solve load disaggregation problem of CleanTech Building One’s HVAC system. In Chapter 6, a new method is proposed to evaluate the accuracy of non-intrusive monitoring.en_US
dc.format.extent68 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industriesen_US
dc.titleSmart metering data analytics for non-intrusive load monitoringen_US
dc.typeThesis
dc.contributor.supervisorXu Yanen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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