Abstract
Classification of different power consumers is a very important task in smart power transmission grids as the different type of consumers may be treated with different conditions. Furthermore, the power suppliers can use the category information of consumers to forecast better their behavior which is a relevant task for load balancing.
In this paper, we present performance results on the classification of consumers using deep learning based classification scheme in smart grid systems. The results are compared with existing classification methods using real, measured power consumption data.
We demonstrate that consumer classification performed by neural networks can outperform existing, traditional tools as in several cases the correct class assignment rate is greater than 0.97.
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This publication/research has been supported by PPKE KAP 16-71009-1.2-ITK Grant. This source of support is gratefully acknowledged.
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Tornai, K., Oláh, A., Drenyovszki, R., Kovács, L., Pintér, I., Levendovszky, J. (2017). Deep Learning Based Consumer Classification for Smart Grid. In: Lau, E., et al. Smart Grid Inspired Future Technologies. SmartGift 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-319-61813-5_13
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DOI: https://doi.org/10.1007/978-3-319-61813-5_13
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