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|Title:||Smart metering data analytics for non-intrusive load monitoring||Authors:||Ding, Hongyuan||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries||Issue Date:||2019||Abstract:||Non-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.||URI:||http://hdl.handle.net/10356/77297||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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