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access icon openaccess Smart meter deployment optimisation and its analysis for appliance load monitoring

In this study, the authors study the problem of smart meter deployment optimisation for appliance load monitoring, that is, to monitor a number of devices without any ambiguity using the minimum number of low-cost smart meters. The importance of this problem is due to the fact that the number of meters should be reduced to decrease the deployment cost, improve reliability and decrease congestion. In this way, in future, smart meters can provide additional information about the type and number of distinct devices connected, besides their normal functionalities concerned with providing overall energy measurements and their communication. The authors present two exact smart meter deployment optimisation algorithms, one based on exhaustive search and the other based on efficient implementation of the exhaustive search. They formulate the problem mathematically and present computational complexity analysis of their algorithms. Simulation scenarios show that for a typical number of home appliances, the efficient search method is significantly faster compared to the exhaustive search and can provide the same optimal solution. The authors also show the dependency of their method on the distribution of the load pattern that can potentially be in a typical household.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 15. Cole, A., Albicki, A.: ‘Nonintrusive identification of electrical loads in a three-phase environment based on harmonic content’. Proc. IEEE Instrumentation and Measurement Technology Conf., 2000, pp. 2429.
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 21. Hao, X., Wang, Y., Wu, C., et al: ‘Smart meter deployment optimization for efficient electrical appliance state monitoring’. Proc. IEEE SmartGridComm, 2012.
    23. 23)
      • 14. Kolter, J., Jaakkola, T.: ‘Approximate inference in additive factorial HMMS with application to energy disaggregation’, J. Mach. Learn. Res., 2012, 22, pp. 14721482.
    24. 24)
      • 5. Arif, A., Al-Hussain, M., Al-Mutairi, N., et al: ‘Experimental study and design of smart energy meter for the smart grid’. Proc. IRSEC, 2013, pp. 515520.
    25. 25)
    26. 26)
    27. 27)
      • 22. Wang, Y., Hao, X., Song, L., et al: ‘Tracking states of massive electrical appliances by lightweight metering and sequence decoding’. Proc. SensorKDD, 2012.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
      • 6. Makonin, S., Popowich, F., Gill, B.: ‘The cognitive power meter: looking beyond the smart meter’. Proc. IEEE CCECE, 2013.
    34. 34)
      • 12. Kim, H., Marwah, M., Arlitt, M., et al: ‘Unsupervised disaggregation of low frequency power measurements’. Proc. DSM, 2010.
    35. 35)
    36. 36)
    37. 37)
      • 11. Parson, O., Ghosh, S., Weal, M., et al: ‘Non-intrusive load monitoring using prior models of general appliance types’. Proc. AAAI-12, 2012.
    38. 38)
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • 25. Marwah, M., Arbitt, M., Lyon, G., et al: ‘Unsupervised disaggregation of low frequency power measurements’. Technical Report, HP labs, 2010.
    43. 43)
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