Load Disaggregation Using Microscopic Power Features and Pattern Recognition

Wesley Angelino de Souza, Fernando Deluno Garcia, Fernando Pinhabel Marafão, Luiz Carlos Pereira da Silva and Marcelo Godoy Simões
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Wesley Angelino de Souza: Department of Computer Science, Federal University of São Carlos (UFSCar), Sorocaba SP 18052-780, Brazil
Fernando Deluno Garcia: Institute of Science and Technology of Sorocaba, São Paulo State University (UNESP), Sorocaba SP 18087-180, Brazil
Fernando Pinhabel Marafão: Institute of Science and Technology of Sorocaba, São Paulo State University (UNESP), Sorocaba SP 18087-180, Brazil
Luiz Carlos Pereira da Silva: School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), Campinas SP 13083-852, Brazil
Marcelo Godoy Simões: Department of Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA

Energies, 2019, vol. 12, issue 14, 1-18

Abstract: 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.

Keywords: load disaggregation; artificial intelligence; cognitive meters; machine learning; state machine; NILM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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