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|Title:||Multi-pattern data mining and recognition of primary electric appliances from single non-intrusive load monitoring data||Authors:||Du, S
|Keywords:||AP clustering algorithm
Power load decomposition
|Issue Date:||2019||Publisher:||Molecular Diversity Preservation International (MDPI)||Source:||Energies, 2019, v. 12, no. 6, en12060992 How to cite?||Journal:||Energies||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.||URI:||http://hdl.handle.net/10397/80910||EISSN:||1996-1073||DOI:||10.3390/en12060992||Rights:||© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Du, S., Li, M., Han, S., Shi, J., & Li, H. (2019). Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data. Energies, 12(6), 992 is available at https://doi.org/10.3390/en12060992
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