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Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations

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  • Drisya, G.V.
  • Asokan, K.
  • Kumar, K. Satheesh

Abstract

Wind speed oscillations are known to exhibit varying characteristics at different time scales. Our recent analysis has shown that a collection of autoregressive models fitted separately on the frequency components of wind speed data can significantly increase the prediction accuracy. In this paper, we report the results of the investigation of dynamical behaviour across a broad frequency spectrum of wind speed measurements. The results show the existence of diverse characteristics such as stochastic, deterministic and chaotic behaviour apart from the variation of the dimensionality of underlying dynamics as well as the degree of fluctuations. It is also demonstrated that a cluster of deterministic models built upon separate frequency components of a wind speed time series can enhance the prediction accuracy by as much as 80%, on the average, consistently for predictions up to 12 h. The comparison shows the definite advantage of deterministic prediction models over autoregressive models. The f-index introduced in this paper to measure the fluctuations of wind speed over a period indicates that the observed seasonal variations of prediction errors can be correlated with changes in the f-index of the component series contributed mostly by the lower scales of decomposition.

Suggested Citation

  • Drisya, G.V. & Asokan, K. & Kumar, K. Satheesh, 2018. "Diverse dynamical characteristics across the frequency spectrum of wind speed fluctuations," Renewable Energy, Elsevier, vol. 119(C), pages 540-550.
  • Handle: RePEc:eee:renene:v:119:y:2018:i:c:p:540-550
    DOI: 10.1016/j.renene.2017.12.024
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    1. Kiplangat, Dennis C. & Asokan, K. & Kumar, K. Satheesh, 2016. "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, Elsevier, vol. 93(C), pages 38-44.
    2. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    3. Monfared, Mohammad & Rastegar, Hasan & Kojabadi, Hossein Madadi, 2009. "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Elsevier, vol. 34(3), pages 845-848.
    4. Mohandes, Mohamed A. & Rehman, Shafiqur & Halawani, Talal O., 1998. "A neural networks approach for wind speed prediction," Renewable Energy, Elsevier, vol. 13(3), pages 345-354.
    5. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    6. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    7. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
    8. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    9. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    10. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    11. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    12. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    13. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    14. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    15. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    16. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    17. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
    18. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
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    3. Stosic, Tatijana & Telesca, Luciano & Stosic, Borko, 2021. "Multiparametric statistical and dynamical analysis of angular high-frequency wind speed time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
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    6. Qin Chen & Yan Chen & Xingzhi Bai, 2020. "Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind," Energies, MDPI, vol. 13(21), pages 1-23, October.

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