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Energies 2019, 12(6), 992; https://doi.org/10.3390/en12060992

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

1
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300354, China
2
College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
3
Department of Building and Real Estate, Hong Kong Polytechnic University, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
Received: 4 January 2019 / Revised: 24 February 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
(This article belongs to the Section Electrical Power and Energy System)
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Abstract

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally. View Full-Text
Keywords: electrical appliance; pattern recognition; data mining; AP clustering algorithm; power load decomposition electrical appliance; pattern recognition; data mining; AP clustering algorithm; power load decomposition
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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 (CC BY 4.0).
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Du, S.; Li, M.; Han, S.; Shi, J.; Li, H. Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data. Energies 2019, 12, 992.

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