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Stochastic generation of hourly mean wind speed data

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  • Aksoy, Hafzullah
  • Fuat Toprak, Z
  • Aytek, Ali
  • Erdem Ünal, N

Abstract

Use of wind speed data is of great importance in civil engineering, especially in structural and coastal engineering applications. Synthetic data generation techniques are used in practice for cases where long wind speed data are required. In this study, a new wind speed data generation scheme based upon wavelet transformation is introduced and compared to the existing wind speed generation methods namely normal and Weibull distributed independent random numbers, the first- and second-order autoregressive models, and the first-order Markov chain. Results propose the wavelet-based approach as a wind speed data generation scheme to alternate the existing methods.

Suggested Citation

  • Aksoy, Hafzullah & Fuat Toprak, Z & Aytek, Ali & Erdem Ünal, N, 2004. "Stochastic generation of hourly mean wind speed data," Renewable Energy, Elsevier, vol. 29(14), pages 2111-2131.
    • Handle: RePEc:eee:renene:v:29:y:2004:i:14:p:2111-2131
    DOI: 10.1016/j.renene.2004.03.011
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    References listed on IDEAS

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    1. Mehmetcik Bayazit & Hafzullah Aksoy, 2001. "Using wavelets for data generation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(2), pages 157-166.
    2. Hafzullah Aksoy, 2001. "Storage Capacity for River Reservoirs by Wavelet-Based Generation of Sequent-Peak Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 15(6), pages 423-437, December.
    3. Sfetsos, A., 2000. "A comparison of various forecasting techniques applied to mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 21(1), pages 23-35.
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    1. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.
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    8. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    9. Cabello, M. & Orza, J.A.G., 2010. "Wind speed analysis in the province of Alicante, Spain. Potential for small-scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 3185-3191, December.
    10. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    11. Li, Jinhua & Li, Chunxiang & He, Liang & Shen, Jianhong, 2015. "Extended modulating functions for simulation of wind velocities with weak and strong nonstationarity," Renewable Energy, Elsevier, vol. 83(C), pages 384-397.
    12. Higinio Sánchez-Sáinz & Carlos-Andrés García-Vázquez & Francisco Llorens Iborra & Luis M. Fernández-Ramírez, 2019. "Methodology for the Optimal Design of a Hybrid Charging Station of Electric and Fuel Cell Vehicles Supplied by Renewable Energies and an Energy Storage System," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
    13. Srikanth Bashetty & Selahattin Ozcelik, 2021. "Review on Dynamics of Offshore Floating Wind Turbine Platforms," Energies, MDPI, vol. 14(19), pages 1-30, September.
    14. Yuan, Shengxi & Kocaman, Ayse Selin & Modi, Vijay, 2017. "Benefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicente," Renewable Energy, Elsevier, vol. 105(C), pages 167-174.
    15. Carapellucci, Roberto & Giordano, Lorena, 2013. "A new approach for synthetically generating wind speeds: A comparison with the Markov chains method," Energy, Elsevier, vol. 49(C), pages 298-305.
    16. Ouammi, Ahmed & Ghigliotti, Valeria & Robba, Michela & Mimet, Abdelaziz & Sacile, Roberto, 2012. "A decision support system for the optimal exploitation of wind energy on regional scale," Renewable Energy, Elsevier, vol. 37(1), pages 299-309.
    17. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    18. Kulwinder Parmar & Rashmi Bhardwaj, 2015. "River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 17-33, January.
    19. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    20. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    21. Salami, Akim Adekunle & Ajavon, Ayite Senah Akoda & Kodjo, Mawugno Koffi & Bedja, Koffi-Sa, 2013. "Contribution to improving the modeling of wind and evaluation of the wind potential of the site of Lome: Problems of taking into account the frequency of calm winds," Renewable Energy, Elsevier, vol. 50(C), pages 449-455.
    22. Zubi, Ghassan, 2011. "Technology mix alternatives with high shares of wind power and photovoltaics—case study for Spain," Energy Policy, Elsevier, vol. 39(12), pages 8070-8077.

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