Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Modelling and forecasting of electric daily peak load movement based on the elliptic-orbit model with weekly periodic extension: a case study

Electric load movement analysis is an important task for effective operation and planning of power systems. It is shown that a weekly quasi-periodicity is enclosed in electric daily peak load movement. On the basis of weekly periodic extension for electric daily peak load movement, the so-called elliptic-orbit model is introduced to describe its movement. Electric daily peak load movement as time series is mapped into the polar coordinates to form the elliptic-orbit model, in which each 7-day-movement is depicted as one elliptic orbit. Experimental results of the Great Britain National Grid and Analysis indicate workability and effectiveness of the proposed method. It is shown that the electric daily peak load movement with weekly periodic extension is well described by the elliptic-orbit model, which presents a vivid description for analysing electric daily peak load movement in a concise and intuitive way, and others may benefit from it.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 19. Kanchan, A., Singh, K.B.: ‘Load modeling, estimation and forecasting’. Universities Power Engineering Conf. (UPEC), Cardiff, Wales, UK, 31August—3 September 2010, pp. 14.
    5. 5)
      • 7. Ghods, L., Kalantar, M.: ‘Different methods of long-term electric load demand forecasting; a comprehensive review’, Iran. J. Electr. Electron. Eng., 2011, 7, (4), pp. 249259.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 34. The Great Britain National Grid: ‘Metered half-hourly electricity demands’, http://www.nationalgrid.com/uk/Electricity/Data/Demand+Data, retrieved on March 17, 2013.
    14. 14)
      • 30. StatSoft, Inc: ‘Electronic statistics textbook’. (StatSoft., Tulsa, OK, 2013) http://www.statsoft.com/textbook/, accessed March 2013.
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 25. Dillon, T.S., Niebur, D.: ‘Artificial neural networks with applications to power systems’ (CRL Pub., London, 1996).
    22. 22)
      • 33. Yang, Z.-C.: ‘Electric load evaluation and forecast based on the elliptic orbit algorithmic model’, Int. J. Electr. Power Energy Syst., 2012, 42, (1), pp. 560567.
    23. 23)
    24. 24)
      • 1. Feinberg, E.A., Genethliou, D.: ‘Load forecasting, applied mathematics for restructured electric power systems: optimization, control, and computational intelligence’ (Springer, 2005), pp. 269285.
    25. 25)
    26. 26)
    27. 27)
      • 8. Liao, N.-H., Hu, Z.-H., Ma, Y.-Y., Lu, W.-Y.: ‘Review of the short-term load forecasting methods of electric power system’, Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2011, 39, (1), pp. 147152.
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
      • 35. The Balancing Mechanism Reporting System (BMRS): http://www.bmreports.com, retrieved on March 17, 2013.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2014.0289
Loading

Related content

content/journals/10.1049/iet-gtd.2014.0289
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address