SMART HOME - From User's Behavior to Prediction of Energy Consumption

Lamis Hawarah, Mirieille Jacomino, Stephane Ploix

2010

Abstract

This paper concerns a home automation system of energy management. Such a system aims at keeping under control the energy consumption in housing. The expected energy consumption is scheduled over one day. Each hour a total amount of energy is available that is a resource constraint for the expected energy plan. The expected consumption is totally derived from users behavior which are quite different from one housing to another, and rather difficult to predict. This paper proposes a Learning System to predict the user's requests of energy. The proposed method relies on Bayesian networks.

References

  1. Abras, S., Ploix, S., Pesty, S., and Jacomino, M. (2007). A multi-agent design for a home automation system dedicated to power management. In Proceedings of the IFIP Conference on Artificial Intelligence Applications and Innovations, Athen, Greece. Springer.
  2. Abras, S., Ploix, S., Pesty, S., and Jacomino, M. (2008). An anticipation mechanism for power management in a smart home using multi-agent systems. In ICTTA'08: Proceedings of the 3rd International Conference on Information and Communication Technologies: from Theory to Applications, pages 110-116, Damascus, Syria. IEEE Computer Society.
  3. Barco, R., Nielsen, L., Guerrero, R., Hylander, G., and Patel, S. (2002). Automated troubleshooting of a mobile communication network using bayesian networks. Mobile and Wireless Communications Network, 2002. 4th International Workshop on, pages 606 - 610.
  4. Becker, A., Geiger, D., Schffer, A. A., and Schaffer, A. A. (1998). Automatic selection of loop breakers for genetic linkage analysis. Human Heredity, 48:49-60.
  5. Ezawa, K. and Schuermann, T. (1995). Fraud/uncollectible debt detection using a bayesian network based learning system: A. In Proceedings of the 11th Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), pages 157-16, San Francisco, CA. Morgan Kaufmann.
  6. HA, L., Ploix, S., Zamai, E., and Jacomino, M. (2006). A home automation system to improve the household energy control. In In INCOM2006 12th IFAC Symposium of Information Control Problems in Manufacturing, Saint Etienne, France.
  7. Ha, S., Jung, H., and Oh, Y. (2006). Method to analyze user behavior in home environment. Personal Ubiquitous Comput., 10(2-3):110-121.
  8. Ha, D. L.; Ploix, S. Z. E. . J. M. (2005). Control of energy consumption in home automation by ressource constraint scheduling. In The 15th International Conference on Control System and ComputerScience.
  9. Hart, P. E. and Graham, J. (1997). Query-free information retrieval. IEEE Intelligent Systems, 12(5):32-37.
  10. Heckerman, D. (1995). A tutorial on learning bayesian networks. Technical report, Communications of the ACM.
  11. Horvitz, E. and Barry, M. (1995). Display of information for time-critical decision making. In In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 296-305. Morgan Kaufmann.
  12. Naïm, P., Wuillemin, P.-H., Leray, P., Pourret, O., and Becker, A. (2004). Réseaux bayésiens. Eyrolles, Paris.
  13. Palensky, P., Dietrich, D., Posta, R., and Reiter, H. (1997). Demand side management in private homes by using lonworks. Vortrag: WFCS97 2nd IEEE Workshop on Factory Communication Systems, Barcelona, pages 341 - 347.
  14. Pearl, J. (1986). Fusion, propagation, and structuring in belief networks. Artif. Intell., 29(3):241-288.
  15. Russell, S. J. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Pearson Education.
  16. Wood, G. and Newborough, M. (2003). Dynamic energyconsumption indicators for domestic appliances: environment, behaviour and design. Energy and Buildings, pages 821-841.
  17. Wood, G. and Newborough, M. (2007). Influencing user behaviour with energy information display systems for intelligent homes. International journal of energy research, vol. 31, no1:56-78.
  18. Zaane, O. R. (1999). Principles of Knowledge Discovery in Databases - Chapter 8: Data Clustering.
  19. Zimmerman, G. (2007). Modeling and simulation of individual user behavior for building performance predictions. In SCSC: Proceedings of the 2007 summer computer simulation conference, pages 913-920, San Diego, CA, USA. Society for Computer Simulation International.
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Paper Citation


in Harvard Style

Hawarah L., Jacomino M. and Ploix S. (2010). SMART HOME - From User's Behavior to Prediction of Energy Consumption . In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-00-3, pages 147-153. DOI: 10.5220/0002947901470153


in Bibtex Style

@conference{icinco10,
author={Lamis Hawarah and Mirieille Jacomino and Stephane Ploix},
title={SMART HOME - From User's Behavior to Prediction of Energy Consumption},
booktitle={Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2010},
pages={147-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002947901470153},
isbn={978-989-8425-00-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - SMART HOME - From User's Behavior to Prediction of Energy Consumption
SN - 978-989-8425-00-3
AU - Hawarah L.
AU - Jacomino M.
AU - Ploix S.
PY - 2010
SP - 147
EP - 153
DO - 10.5220/0002947901470153