Abstract
Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. In this work a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.
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Acknowledgements
This study was performed with support from the National Council for Scientific and Technological Development—CNPq Project 409687/2018–9.
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Ferraz, F.C., Monteiro, R.V.A., Teixeira, R.F.S. et al. A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring. J Control Autom Electr Syst 34, 842–857 (2023). https://doi.org/10.1007/s40313-023-00999-2
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DOI: https://doi.org/10.1007/s40313-023-00999-2