Deep Learning Based Consumer Classification for Smart Grid

Kálmán Tornai; András Oláh; Rajmund Drenyovszki; Lóránt Kovács; István Pintér; János Levendovszky

Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27–28, 2017, Proceedings

Research Article

Deep Learning Based Consumer Classification for Smart Grid

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@INPROCEEDINGS{10.1007/978-3-319-61813-5_13,
    author={K\^{a}lm\^{a}n Tornai and Andr\^{a}s Ol\^{a}h and Rajmund Drenyovszki and L\^{o}r\^{a}nt Kov\^{a}cs and Istv\^{a}n Pint\^{e}r and J\^{a}nos Levendovszky},
    title={Deep Learning Based Consumer Classification for Smart Grid},
    proceedings={Smart Grid Inspired Future Technologies. Second EAI International Conference, SmartGIFT 2017, London, UK, March 27--28, 2017, Proceedings},
    proceedings_a={SMARTGIFT},
    year={2017},
    month={9},
    keywords={Classification methods Consumer classification Deep learning Softmax layer network},
    doi={10.1007/978-3-319-61813-5_13}
}

Kálmán Tornai
András Oláh
Rajmund Drenyovszki
Lóránt Kovács
István Pintér
János Levendovszky
Year: 2017
Deep Learning Based Consumer Classification for Smart Grid
SMARTGIFT
Springer
DOI: 10.1007/978-3-319-61813-5_13

Kálmán Tornai¹^,*, András Oláh¹, Rajmund Drenyovszki², Lóránt Kovács², István Pintér², János Levendovszky³

1: Pázmány Péter Catholic University
2: Pallas Athene University
3: Budapest University of Technology and Economics

*Contact email: tornai.kalman@itk.ppke.hu

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.

Keywords: Classification methods Consumer classification Deep learning Softmax layer network

Published: 2017-09-19
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-61813-5_13