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Quaternion-valued short-term joint forecasting of three-dimensional wind and atmospheric parameters

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  • Took, C. Cheong
  • Strbac, G.
  • Aihara, K.
  • Mandic, D.P.

Abstract

This work introduces novel methodology for the simultaneous modelling and forecasting of three-dimensional wind field. This is achieved based on a quaternion wind model, which by virtue of its division algebra accounts naturally for the coupling between the three wind dimensions. To fully exploit the available second order statistics, we employ the newly developed augmented quaternion statistics and perform prediction based on the widely linear model. The proposed quaternion domain processing also facilitates the fusion of external atmospheric parameters, such as air temperature, yielding improved forecasts. Simulations for wind regimes with different dynamics and over a range of prediction horizons, together with the fusion of air temperature, support the approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:6:p:1754-1760
    DOI: 10.1016/j.renene.2010.12.013
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    References listed on IDEAS

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    1. 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.
    2. Kantz, Holger & Holstein, Detlef & Ragwitz, Mario & K. Vitanov, Nikolay, 2004. "Markov chain model for turbulent wind speed data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(1), pages 315-321.
    3. 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.
    4. 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.
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    Cited by:

    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Jinlong Shu & Lianglin Xiong & Tao Wu & Zixin Liu, 2019. "Stability Analysis of Quaternion-Valued Neutral-Type Neural Networks with Time-Varying Delay," Mathematics, MDPI, vol. 7(1), pages 1-23, January.
    3. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Rashid Al Abri & Swaroop Gajare & Sultan Al Yahyai & Mostafa Bakhtvar, 2021. "A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.

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