DocumentCode
1336096
Title
Load Signature Study—Part II: Disaggregation Framework, Simulation, and Applications
Author
Liang, Jian ; Ng, Simon K.K. ; Kendall, Gail ; Cheng, John W.M.
Author_Institution
CLP Res. Inst. Ltd., Hong Kong, China
Volume
25
Issue
2
fYear
2010
fDate
4/1/2010 12:00:00 AM
Firstpage
561
Lastpage
569
Abstract
Load signatures embedded in common electricity consumption patterns, in fact, could render much information pertaining to the nature of the appliances and their usage patterns. Based on the proposed disaggregation framework, we use three advanced disaggregation algorithms, called committee decision mechanisms (CDMs), to perform load disaggregation at the metering level. Three random switching simulators are also developed to investigate the performance of different CDMs under a variety of scenarios. Through Monte Carlo simulations, we demonstrate that all CDMs outperform any single-feature, single-algorithm-based disaggregation methods. With sensitivity analysis, we also show that the CDMs are less sensitive to any load dynamics and noise. We finally demonstrate some applications of this technology in terms of appliance usage tacking and estimated energy consumption of each appliance.
Keywords
Monte Carlo methods; domestic appliances; load management; power consumption; sensitivity analysis; Monte Carlo simulations; appliance usage tacking; committee decision mechanisms; electricity consumption patterns; energy consumption; load disaggregation algorithms; load signatures; random switching simulators; sensitivity analysis; Committee decision mechanism; Monte Carlo methods; electric-load intelligence; load disaggregation; load signature; smart metering;
fLanguage
English
Journal_Title
Power Delivery, IEEE Transactions on
Publisher
ieee
ISSN
0885-8977
Type
jour
DOI
10.1109/TPWRD.2009.2033800
Filename
5337970
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