IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v29y2004i8p1407-1418.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148103001435
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/S0960-1481(03)00143-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:29:y:2004:i:8:p:1407-1418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.