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On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids

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Advances in Artificial Intelligence (CAEPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9422))

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Abstract

Along with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying consumption patterns towards improving the way the energy is produced and consumed (via e.g. demand side management strategies). Such data can be extracted by using smart meters, but the expensive cost of incorporating a monitoring device for each appliance jeopardizes significantly the massive implementation of any straightforward approach. When resorting to a single meter to monitor the global consumption of the house at hand, the identification of the different appliances giving rise to the recorded consumption profile renders a particular instance of the so-called source separation problem, for which a number of algorithmic proposals have been reported in the literature. This paper gravitates on the applicability of the Ant Colony Optimization (ACO) algorithm to perform this power disaggregation treating the problem as a Constraint Satisfaction Problem (CSP). The discussed experimental results utilize data contained in the REDD dataset, which corresponds to real power consumption traces of different households. Although the experiments carried out in this work reveal that the ACO solver can be successfully applied to the Non-Intrusive Load Monitoring problems, further work is needed towards assessing its performance when tackling more diverse appliance models and noisy power load traces.

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Acknowledgements

This work is supported by the Spanish Ministry of Science and Education under grant number TIN2014-56494-C4-4-P, the Comunidad Autonoma de Madrid under the CIBERDINE project (S2013/ICE-3095), Airbus Defense & Space projects FUAM-076914 and FUAM-076915, and the Basque Government under the Etortek Programme.

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Authors and Affiliations

  1. Basque Center for Applied Mathematics (BCAM), Alameda de Mazarredo, 14, 48009, Bilbao, Basque Country, Spain

    Antonio Gonzalez-Pardo

  2. OPTIMA Area, TECNALIA, 48160, Derio, Spain

    Antonio Gonzalez-Pardo & Javier Del Ser

  3. Computer Science Department, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain

    David Camacho

Authors
  1. Antonio Gonzalez-Pardo
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  2. Javier Del Ser
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  3. David Camacho
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Corresponding author

Correspondence to Antonio Gonzalez-Pardo .

Editor information

Editors and Affiliations

  1. University of Castilla-La Mancha, Albacete, Spain

    José M. Puerta

  2. University of Castilla-La Mancha, Albacete, Spain

    José A. Gámez

  3. University of Cadiz, Cadiz, Spain

    Bernabe Dorronsoro

  4. Public University of Navarre, Pamplona, Spain

    Edurne Barrenechea

  5. Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso

  6. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Bruno Baruque

  7. Public University of Navarre, Pamplona, Spain

    Mikel Galar

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Gonzalez-Pardo, A., Del Ser, J., Camacho, D. (2015). On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids. In: Puerta, J., et al. Advances in Artificial Intelligence. CAEPIA 2015. Lecture Notes in Computer Science(), vol 9422. Springer, Cham. https://doi.org/10.1007/978-3-319-24598-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-24598-0_28

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-24598-0

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