Smart metering data analytics for non-intrusive load monitoring
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...
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sg-ntu-dr.10356-772972023-07-04T16:18:39Z Smart metering data analytics for non-intrusive load monitoring Ding, Hongyuan Xu Yan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries 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. Master of Science (Computer Control and Automation) 2019-05-24T04:15:21Z 2019-05-24T04:15:21Z 2019 Thesis http://hdl.handle.net/10356/77297 en 68 p. application/pdf |
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Nanyang Technological University |
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NTU Library |
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Asia |
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Singapore Singapore |
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English |
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DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries |
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DRNTU::Engineering::Electrical and electronic engineering::Electric power::Auxiliaries, applications and electric industries Ding, Hongyuan Smart metering data analytics for non-intrusive load monitoring |
| description |
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. |
| author2 |
Xu Yan |
| author_facet |
Xu Yan Ding, Hongyuan |
| format |
Theses and Dissertations |
| author |
Ding, Hongyuan |
| author_sort |
Ding, Hongyuan |
| title |
Smart metering data analytics for non-intrusive load monitoring |
| title_short |
Smart metering data analytics for non-intrusive load monitoring |
| title_full |
Smart metering data analytics for non-intrusive load monitoring |
| title_fullStr |
Smart metering data analytics for non-intrusive load monitoring |
| title_full_unstemmed |
Smart metering data analytics for non-intrusive load monitoring |
| title_sort |
smart metering data analytics for non-intrusive load monitoring |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10356/77297 |
| _version_ |
1772828092275884032 |