Abstract
Measuring and recording systems for the consumption of electrical energy which are connected to households, are essential in the optimization of energy use. Non-Intrusive Load Monitoring (NILM) is one of the most used techniques in the study of electrical consumption; these systems are based on the analysis of the load curve (the aggregated electrical consumption of the whole household). Thanks to a significant reduction in the price of sensors and sensor systems in recent years, it is possible to individually monitor each one of the devices connected to the grid. In this paper we compare different classifiers in order to find out which is the most appropriate for the identification of individual appliances attending to their consumption. In this way, we will know which electrical appliance is connected to a smart plug, helping to obtain more accurate and efficient load monitoring systems.
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Acknowledgements
This work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Skłodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref 641794.
The research of Alberto L. Barriuso has been co-financed by the European Social Fund (Operational Programme 2014–2020 for Castilla y León, EDU/128/2015 BOCYL).
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de la Iglesia, D.H. et al. (2018). Single Appliance Automatic Recognition: Comparison of Classifiers. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_11
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DOI: https://doi.org/10.1007/978-3-319-61578-3_11
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