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Complex-valued prediction of wind profile using augmented complex statistics

Author

Listed:
  • Mandic, D.P.
  • Javidi, S.
  • Goh, S.L.
  • Kuh, A.
  • Aihara, K.

Abstract

This paper presents a novel approach for the simultaneous modelling and forecasting of wind whereby the wind field is considered as a vector of its speed and direction components in the field of complex numbers C. To account for the intermittency and coupling of wind speed and direction, we propose to use the recently introduced framework of augmented complex statistics. The augmented complex least mean square (ACLMS) algorithm is introduced and its usefulness in wind forecasting is analysed. Simulations over different wind regimes support the approach.

Suggested Citation

  • Mandic, D.P. & Javidi, S. & Goh, S.L. & Kuh, A. & Aihara, K., 2009. "Complex-valued prediction of wind profile using augmented complex statistics," Renewable Energy, Elsevier, vol. 34(1), pages 196-201.
    • Handle: RePEc:eee:renene:v:34:y:2009:i:1:p:196-201
    DOI: 10.1016/j.renene.2008.03.022
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    References listed on IDEAS

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    1. Goh, S.L. & Chen, M. & Popović, D.H. & Aihara, K. & Obradovic, D. & Mandic, D.P., 2006. "Complex-valued forecasting of wind profile," Renewable Energy, Elsevier, vol. 31(11), pages 1733-1750.
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    Cited by:

    1. Khalid, M. & Savkin, A.V., 2010. "A model predictive control approach to the problem of wind power smoothing with controlled battery storage," Renewable Energy, Elsevier, vol. 35(7), pages 1520-1526.
    2. Took, C. Cheong & Strbac, G. & Aihara, K. & Mandic, D.P., 2011. "Quaternion-valued short-term joint forecasting of three-dimensional wind and atmospheric parameters," Renewable Energy, Elsevier, vol. 36(6), pages 1754-1760.
    3. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    4. Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J., 2021. "Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions," International Journal of Forecasting, Elsevier, vol. 37(2), pages 812-824.
    5. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.

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