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Abstract

It is possible to detect the presence of residents in a home by monitoring its energy consumption. Currently, the state of the art provides us with a number of approaches. Some studies leverage intrusive systems which require user interaction. Others employ sensors to detect the presence of people in a non-intrusive way. In this article, we propose the use of a sensor network for measuring electric energy consumption in a home. A multi-agent system is used to manage the data generated by the deployed sensor network in an intelligent way. A non-intrusive occupation monitoring algorithm was designed to determine when a house is occupied and when it is empty.

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References

  1. Chaney, J., Hugh Owens, E., Peacock, A.D.: An evidence based approach to determining residential occupancy and its role in demand response management. Energy Build. 125, 254–266 (2016)

    Article  Google Scholar 

  2. Tang, G., Wu, K., Lei, J., Xiao, W.: SHARK: sparse human action recovery with knowledge of appliances and load curve data. Cyber-Phys. Syst. 1(2–4), 113–131 (2015)

    Article  Google Scholar 

  3. Kleiminger, W., Beckel, C., Santini, S.: Household occupancy monitoring using electricity meters. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 2015, pp. 975–986 (2015)

    Google Scholar 

  4. Federal Energy Regulatory Commission: Assessment of Demand Response & Advanced Metering (2015)

    Google Scholar 

  5. Junnila, S., Kailanto, H., Merilahti, J., Vainio, A.M., Vehkaoja, A., Zakrzewski, M., Hyttinen, J.: Wireless, multipurpose in-home health monitoring platform: two case trials. IEEE Trans. Inf. Technol. Biomed. 14(2), 447–455 (2010)

    Article  Google Scholar 

  6. Kaushik, A.R., Celler, B.G.: Characterization of PIR detector for monitoring occupancy patterns and functional health status of elderly people living alone at home. Technol. Health Care 15(4), 273–288 (2007)

    Google Scholar 

  7. Gellings, C.W.: The Smart Grid : Enabling Energy Efficiency and Demand Response. Fairmont Press (2009)

    Google Scholar 

  8. European Comission: Strategic Energy Technology Plan. https://ec.europa.eu/energy/en/topics/technology-and-innovation/strategic-energy-technology-plan

  9. U.S. Government: Energy Independence and Security Act

    Google Scholar 

  10. Tang, G., Wu, K., Lei, J., Bi, Z., Tang, J.: From landscape to portrait: a new approach for outlier detection in load curve data. IEEE Trans. Smart Grid 5(4), 1764–1773 (2014)

    Article  Google Scholar 

  11. Stankovic, L., Stankovic, V., Liao, J., Wilson, C.: Measuring the energy intensity of domestic activities from smart meter data. Appl. Energy 183, 1565–1580 (2016)

    Article  Google Scholar 

  12. Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P.: Non-intrusive occupancy monitoring using smart meters. In: 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings – BuildSys 2013, pp. 1–8 (2013)

    Google Scholar 

  13. Cloogy—Eficiência Energética—Residências e Empresas. https://www.cloogy.pt/. Accessed 20 Jan 2018

  14. Labeodan, T., Zeiler, W., Boxem, G., Zhao, Y.: Occupancy measurement in commercial office buildings for demand-driven control applications—A survey and detection system evaluation. Energy Build. 93, 303–314 (2015)

    Article  Google Scholar 

  15. Uribe-Pérez, N., Hernández, L., de la Vega, D., Angulo, I.: State of the art and trends review of smart metering in electricity grids. Appl. Sci. 6(3), 1–24 (2016)

    Article  Google Scholar 

  16. Zato, C., Villarrubia, G., Sánchez, A., Barri, I., et al.: PANGEA – platform for automatic construction of organizations of intelligent agents. In: Omatu, S., De Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 229–239. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28765-7_27

    Chapter  Google Scholar 

  17. VPS - Energy Efficiency and Automated Demand Response. https://www.vps.energy/. Accessed 20 Jan 2018

  18. Aftab, M., Chau, C.-K.: Smart power plugs for efficient online classification and tracking of appliance behavior (2017)

    Google Scholar 

  19. de la Iglesia, D.H., Barriuso, A.L., Murciego, Á.L., Herrero, J.R., Landeck, J., de Paz, J.F., Corchado, J.M.: Single appliance automatic recognition: comparison of classifiers. In: De la Prieta, F., Vale, Z., Antunes, L., Pinto, T., Campbell, A.T., Julián, V., Neves, A.J.R., Moreno, M.N. (eds.) PAAMS 2017. AISC, vol. 619, pp. 115–124. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_11

    Chapter  Google Scholar 

  20. Uttama Nambi, A.S.N., Reyes Lua, A., Prasad, V.R.: LocED. In: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys 2015, pp. 45–54 (2015)

    Google Scholar 

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Acknowledgements

The present work was done and funded in the scope of H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No. 641794). The research of Daniel Hernández de la Iglesia has been co-financed by the European Social Fund and Junta de Castilla y León (Operational Programme 2014–2020 for Castilla y León, EDU/529/2017 BOCYL). Álvaro Lozano is supported by the pre-doctoral fellowship from the University of Salamanca and Banco Santander. This work was supported by the Spanish Ministry, Ministerio de Economía y Competitividad and FEDER funds. The research of Alberto López Barriuso has been co-financed by the European Social Fund and Junta de Castilla y León (Operational Programme 2014–2020 for Castilla y León, EDU/128/2015 BOCYL).

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Correspondence to Alberto L. Barriuso .

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Barriuso, A.L., Lozano, Á., de la Iglesia, D.H., Villarrubia, G., de Paz, J.F. (2018). Household Occupancy Detection Based on Electric Energy Consumption. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_20

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  • DOI: https://doi.org/10.1007/978-3-319-94779-2_20

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