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Complex-valued forecasting of wind profile

Author

Listed:
  • Goh, S.L.
  • Chen, M.
  • Popović, D.H.
  • Aihara, K.
  • Obradovic, D.
  • Mandic, D.P.

Abstract

This paper presents a novel approach for the simultaneous modelling and forecasting of wind signal components. This is achieved in the complex domain by using novel neural network algorithms and architectures. We first perform a signal nonlinearity and component-dependent analyses, which suggest the use of modular complex-valued recurrent neural networks (RNNs). This RNN-based modelling rests upon a combination of nonlinearity, complexity and internal memory and allows for the multiple step ahead forecasting of the wind signal in its complex form (speed and direction). The approach is first verified on benchmark Data Set A (NH3 laser data) of the Santa Fe Time Series Prediction Competition together with artificial data generated by chaotic Mackey–Glass equations, and then applied to the real-world wind measurements. Simulations support the proposed architecture and algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:11:p:1733-1750
    DOI: 10.1016/j.renene.2005.07.006
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    References listed on IDEAS

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    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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    Cited by:

    1. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Global Stability Analysis of Fractional-Order Quaternion-Valued Bidirectional Associative Memory Neural Networks," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    2. Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
    3. Salcedo-Sanz, Sancho & Ángel M. Pérez-Bellido, & Ortiz-García, Emilio G. & Portilla-Figueras, Antonio & Prieto, Luis & Paredes, Daniel, 2009. "Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction," Renewable Energy, Elsevier, vol. 34(6), pages 1451-1457.
    4. Yu, Chuanjin & Li, Yongle & Zhao, Liyang & Chen, Qian & Xun, Yuxing, 2023. "A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions," Energy, Elsevier, vol. 262(PB).
    5. Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo & Monteiro, Cláudio & Sousa, João & Bessa, Ricardo, 2009. "Comparison of two new short-term wind-power forecasting systems," Renewable Energy, Elsevier, vol. 34(7), pages 1848-1854.
    6. Guo, Runan & Zhang, Ziye & Liu, Xiaoping & Lin, Chong, 2017. "Existence, uniqueness, and exponential stability analysis for complex-valued memristor-based BAM neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 100-117.
    7. Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
    8. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    9. Grienggrai Rajchakit & Pharunyou Chanthorn & Pramet Kaewmesri & Ramalingam Sriraman & Chee Peng Lim, 2020. "Global Mittag–Leffler Stability and Stabilization Analysis of Fractional-Order Quaternion-Valued Memristive Neural Networks," Mathematics, MDPI, vol. 8(3), pages 1-29, March.
    10. Cao, Yang & Sriraman, R. & Shyamsundarraj, N. & Samidurai, R., 2020. "Robust stability of uncertain stochastic complex-valued neural networks with additive time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 207-220.
    11. 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.
    12. 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.
    13. Usa Humphries & Grienggrai Rajchakit & Pramet Kaewmesri & Pharunyou Chanthorn & Ramalingam Sriraman & Rajendran Samidurai & Chee Peng Lim, 2020. "Stochastic Memristive Quaternion-Valued Neural Networks with Time Delays: An Analysis on Mean Square Exponential Input-to-State Stability," Mathematics, MDPI, vol. 8(5), pages 1-26, May.
    14. Ren, Yue & Jiang, Haijun & Hu, Cheng & Li, Xinman & Qin, Xuejiao, 2023. "Discontinuous control for exponential synchronization of complex-valued stochastic multi-layer networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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