Load Disaggregation Using Microscopic Power Features and Pattern Recognition
by
Wesley Angelino de Souza 1
, Fernando Deluno Garcia 2, 2,*, Luiz Carlos Pereira da Silva 3 and 4,*
, Fernando Deluno Garcia 2, 2,*
, Luiz Carlos Pereira da Silva 3 and 4,*
1
Department of Computer Science, Federal University of São Carlos (UFSCar), Sorocaba SP 18052-780, Brazil
2
Institute of Science and Technology of Sorocaba, São Paulo State University (UNESP), Sorocaba SP 18087-180, Brazil
3
School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), Campinas SP 13083-852, Brazil
4
Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(14), 2641; https://doi.org/10.3390/en12142641
Received: 29 May 2019
/
Revised: 5 July 2019
/
Accepted: 5 July 2019
/
Published: 10 July 2019
(This article belongs to the Special Issue Applied Neural Networks and Fuzzy Logic in Power Electronics, Motor Drives, Renewable Energy Systems and Smart Grids)
A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.
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Keywords:
load disaggregation; artificial intelligence; cognitive meters; machine learning; state machine; NILM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
de Souza, W.A.; Garcia, F.D.; Marafão, F.P.; da Silva, L.C.P.; Simões, M.G. Load Disaggregation Using Microscopic Power Features and Pattern Recognition. Energies 2019, 12, 2641. https://doi.org/10.3390/en12142641
AMA Style
de Souza WA, Garcia FD, Marafão FP, da Silva LCP, Simões MG. Load Disaggregation Using Microscopic Power Features and Pattern Recognition. Energies. 2019; 12(14):2641. https://doi.org/10.3390/en12142641
Chicago/Turabian Stylede Souza, Wesley A., Fernando D. Garcia, Fernando P. Marafão, Luiz C.P. da Silva, and Marcelo G. Simões. 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition" Energies 12, no. 14: 2641. https://doi.org/10.3390/en12142641
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