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Statistics-Based Approach to Enable Consumer Profile Definition for Demand Response Programs

  • R. A. S. Fernandes
  • L. O. Deus
  • L. Gomes
  • Z. Vale
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 620)

Abstract

This paper presents a statistical analysis of a database generated by voltage and current measurements acquired in a laboratorial environment, which simulates a residential kitchen. In this sense, data were acquired during one month in order to verify both the occurrence of errors as well as the possible identification of the loads. Thus, it is intended that the statistical analysis allows the database to be used to the purposes of Demand Response. However, at first, there was an analysis by histograms in order to verify the occurrence of errors on the measurements and then the feature extraction stage. In the sequence, these features were used to dene decision rules that could perform the identification of loads. The results obtained demonstrated an average precision rate of more than 90%.

Keywords

statiscal analysis non-intrusive load monitoring confidence intervals demand response 

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Notes

Acknowledgements

This paper was supported by FAPESP (grant number 2016/00641–4), CAPES and CNPq.

References

  1. 1. Z. Wang and G Zheng. Residential Appliances Identification and Monitoring by a Nonintrusive Method. IEEE Transactions on Smart Grid, 3(1):80–92, 2012.Google Scholar
  2. 2. M. Dong, P. C. M. Meira, W. Xu, and C. Y. Chung. Non-Intrusive Signature Extraction for Major Residential Loads. IEEE Transactions on Smart Grid, 4(3):142–11430, 2013.Google Scholar
  3. 3. R. A. S. Fernandes, I. N. Silva, and M. Oleskovicz. Load Profile Identification Interface for Consumer Online Monitoring Purposes in Smart Grids. IEEE Transactions on Industrial Informatics, 9(3):1507–1517, 2013.Google Scholar
  4. 4. K. Basu, V. Debusschere, S. Bacha, U. Maulik, and S. Bondyopadhyay. Non Intrusive Load Monitoring: A Temporal Multi-Label Classication Approach. IEEE Transactions on Industrial Informatics, 11(1):262–270, 2015.Google Scholar
  5. 5. A. Anvari-Moghaddam, H. Monsef, and A. Rahimi-Kian. Optimal Smart Home Energy Management Considering Energy Saving and Comfortable Lifestyle. IEEE Transactions on Smart Grid, 6(1):324–332, 2015.Google Scholar
  6. 6. C. Vivekananthan, Y. Mishra, G. Ledwich, and F. Li. Demand Response for Residential Appliances via Customer Reward Scheme. IEEE Transactions on Smart Grid, 5(2):809–820, 2014.Google Scholar
  7. 7. M. Dong, P. C. M. Meira, W. Xu, and W. Freitas. An Event Window Based Load Monitoring Technique for Smart Meters. IEEE Transactions on Smart Grid, 3(2):787–796, 2012.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • R. A. S. Fernandes
    • 1
  • L. O. Deus
    • 1
  • L. Gomes
    • 2
  • Z. Vale
    • 2
  1. 1.Department of Electrical EngineeringFederal University of Sao Carlos - UFSCarSao CarlosBrazil
  2. 2.Knowledge Engineering and Decision Support Research Center – GECADInstitute Polytechnic of Porto – IPPPortoPortugal

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