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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.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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