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A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco

Author

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  • Nfaoui, H.
  • Essiarab, H.
  • Sayigh, A.A.M.

Abstract

Time series of then years of Hourly Average Wind Speed, HAWS, data from Tangiers station are analysed on a statistical basis by applying the Markovian process. The limiting behaviour of the Markov chain is then examined and compared to the histogram of observed wind speed. It was found that a 12×12 transition probability matrix was necessary to generate an acceptable synthetic time series. The manner in which the Markovian model can be used to generate wind speed time series are also described.

Suggested Citation

  • Nfaoui, H. & Essiarab, H. & Sayigh, A.A.M., 2004. "A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco," Renewable Energy, Elsevier, vol. 29(8), pages 1407-1418.
    • Handle: RePEc:eee:renene:v:29:y:2004:i:8:p:1407-1418
    DOI: 10.1016/S0960-1481(03)00143-5
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    Cited by:

    1. Wekesa, David Wafula & Wang, Cong & Wei, Yingjie & Danao, Louis Angelo M., 2017. "Analytical and numerical investigation of unsteady wind for enhanced energy capture in a fluctuating free-stream," Energy, Elsevier, vol. 121(C), pages 854-864.
    2. Zhang, Chen & Gao, Wei & Yang, Tao & Guo, Sheng, 2019. "Opportunistic maintenance strategy for wind turbines considering weather conditions and spare parts inventory management," Renewable Energy, Elsevier, vol. 133(C), pages 703-711.
    3. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
    4. Joan Pau Sierra & Ricard Castrillo & Marc Mestres & César Mösso & Piero Lionello & Luigi Marzo, 2020. "Impact of Climate Change on Wave Energy Resource in the Mediterranean Coast of Morocco," Energies, MDPI, vol. 13(11), pages 1-19, June.
    5. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
    6. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
    7. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    8. Sierra, J.P. & Martín, C. & Mösso, C. & Mestres, M. & Jebbad, R., 2016. "Wave energy potential along the Atlantic coast of Morocco," Renewable Energy, Elsevier, vol. 96(PA), pages 20-32.
    9. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    10. Suomalainen, K. & Silva, C.A. & Ferrão, P. & Connors, S., 2012. "Synthetic wind speed scenarios including diurnal effects: Implications for wind power dimensioning," Energy, Elsevier, vol. 37(1), pages 41-50.
    11. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
    12. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
    13. Zhu, Y. & Li, Y.P. & Huang, G.H., 2012. "Planning municipal-scale energy systems under functional interval uncertainties," Renewable Energy, Elsevier, vol. 39(1), pages 71-84.
    14. 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.
    15. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    16. Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
    17. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
    18. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2017. "Insuring wind energy production," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 542-553.
    19. Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
    20. Chen, C. & Li, Y.P. & Huang, G.H. & Zhu, Y., 2012. "An inexact robust nonlinear optimization method for energy systems planning under uncertainty," Renewable Energy, Elsevier, vol. 47(C), pages 55-66.

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