Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data

Shunichi Hattori, Yasushi Shinohara

2017

Abstract

This paper proposes an actual consumption estimation algorithm that achieves highly accurate occupancy detection using electricity consumption data derived from smart meters. In Japan, electricity consumption data on households will soon be available because smart meters, which enable electric power companies to monitor how much electric power people are using in each household, have been installed in all households. Occupancy detection is a major technique that leverages electricity consumption data and can be applied to various services such as ambient assisted living, sales promotions, and peak load shifting. However, it is difficult to conduct high-accuracy occupancy detection using electricity consumption data automatically derived from smart meters because of their low resolution: 30-min intervals and 100 Wh increments. An actual consumption estimation algorithm is therefore proposed to generate data that reflects the characteristics of a household’s state from low-resolution smart meter data. Occupancy detection is implemented using the estimated consumption data, which are generated by the proposed algorithm, and the results of experiments show that its performance is improved compared to the result obtained using raw smart meter data.

References

  1. Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., and Srivastava, M. (2014). Nilmtk: An open source toolkit for non-intrusive load monitoring. In Proceedings of the 5th International Conference on Future Energy Systems, pages 265- 276. ACM.
  2. Beckel, C., Kleiminger, W., Cicchetti, R., Staake, T., and Santini, S. (2014). The ECO data set and the performance of non-intrusive load monitoring algorithms. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings , pages 80-89. ACM.
  3. Chang, H.-H., Lin, C.-L., and Lee, J.-K. (2010). Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms. In Computer Supported Cooperative Work in Design (CSCWD), 2010 14th International Conference on, pages 27-32. IEEE.
  4. Chen, D., Barker, S., Subbaswamy, A., Irwin, D., and Shenoy, P. (2013). Non-intrusive occupancy monitoring using smart meters. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pages 1-8. ACM.
  5. Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds, M., and Patel, S. (2011). Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Computing, 10(1):28-39.
  6. Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870-1891.
  7. Japkowicz, N. et al. (2000). Learning from imbalanced data sets: A comparison of various strategies. In AAAI Workshop on Learning from Imbalanced Data Sets, volume 68, pages 10-15.
  8. Kim, H., Marwah, M., Arlitt, M. F., Lyon, G., and Han, J. (2011). Unsupervised disaggregation of low frequency power measurements. In SDM, volume 11, pages 747-758. SIAM.
  9. Kleiminger, W., Beckel, C., and Santini, S. (2015). Household occupancy monitoring using electricity meters. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 975-986. ACM.
  10. Komatsu, H. and Nishio, K. (2015). How can smart data analytics improve the way of information provision? -the trend of methods and an exploratory analysis. In Proceedings of 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting.
  11. Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., and Irwin, D. (2010). Private memoirs of a smart meter. In Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-efficiency in Buildings, pages 61-66. ACM.
  12. Nakano, Y. and Murata, H. (2007). Non-intrusive electric appliances load monitoring system using harmonic pattern recognition-trial application to commercial building. In International Conference on Electrical Engineering.
  13. Nguyen, T. A. and Aiello, M. (2013). Energy intelligent buildings based on user activity: A survey. Energy and Buildings, 56:244-257.
  14. Nomura, K., Kashiwagi, T., Yamashita, T., and Kawade, T. (2014). Solution to utilization of smart meter data. FUJITSU Sci. Tech. J, 50(2):58-66.
  15. Rashidi, P. and Mihailidis, A. (2013). A survey on ambientassisted living tools for older adults. IEEE journal of biomedical and health informatics, 17(3):579-590.
  16. Zoha, A., Gluhak, A., Imran, M. A., and Rajasegarar, S. (2012). Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12(12):16838-16866.
Download


Paper Citation


in Harvard Style

Hattori S. and Shinohara Y. (2017). Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data . In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 39-48. DOI: 10.5220/0006129400390048


in Bibtex Style

@conference{sensornets17,
author={Shunichi Hattori and Yasushi Shinohara},
title={Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data},
booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2017},
pages={39-48},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006129400390048},
isbn={978-989-758-211-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Actual Consumption Estimation Algorithm for Occupancy Detection using Low Resolution Smart Meter Data
SN - 978-989-758-211-0
AU - Hattori S.
AU - Shinohara Y.
PY - 2017
SP - 39
EP - 48
DO - 10.5220/0006129400390048