Smart metering data analytics for non-intrusive load monitoring
Date of Issue2019-05-24
School of Electrical and Electronic Engineering
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.
DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries