The objective of the proposed research is to develop an intelligent load modeling, identification, and prediction technology to provide granular load energy consumption and performance details and drive building energy reduction, demand reduction, and proactive equipment maintenance. Electricity consumption in commercial and residential sectors accounts for about 70% of the total electricity generation in United States. Buildings are the most important consumers, and contribute to over 80% of the consumptions in these two sectors. To reduce electrical energy spending and carbon emission, several studies from Pacific Northwest National Lab (PNNL) and National Renewable Energy Lab (NREL) prove that if equipped with the proper technologies, a commercial or a residential building can potentially improve energy savings of buildings by up to about 10% to 30% of their usage. However, the market acceptance of these new technologies today is still not sufficient, and the reason is generally acknowledged to be the lack of solution to quantify the contributions of these new technologies to the energy savings, and the invisibility of the loads in buildings. A non-intrusive load monitoring (NILM) system is proposed in this dissertation, which can identify every individual load in buildings and record the energy consumption, time-of-day variations and other relevant statistics of the identified load, with no access to the individual component. The challenge of such a non-intrusive load monitoring is to find features that are unique for a particular load and then to match a measured feature of an unknown load against a database or library of known. Many problems exist in this procedure and the proposed research is going to focus on three directions to overcome the bottlenecks. They are respectively fundamental load studies for a model-driven feature extraction, adaptive identification algorithms for load space extendibility, and the practical simplifications for the real industrial applications. The simulation results show the great potentials of this new technology in building energy monitoring and management.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54951 |
| Date | 27 May 2016 |
| Creators | He, Dawei |
| Contributors | Habetler, Thomas, Harley, Ronald |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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