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A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site

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  • Carta, José A.
  • Velázquez, Sergio
  • Cabrera, Pedro

Abstract

So-called Measure-Correlate-Predict (MCP) methods have been extensively proposed in renewable energy related literature to estimate the wind resources that represent the long-term conditions at a target site where a short-term wind data measurement campaign has been held.

Suggested Citation

  • Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
  • Handle: RePEc:eee:rensus:v:27:y:2013:i:c:p:362-400
    DOI: 10.1016/j.rser.2013.07.004
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    1. Angelis-Dimakis, Athanasios & Biberacher, Markus & Dominguez, Javier & Fiorese, Giulia & Gadocha, Sabine & Gnansounou, Edgard & Guariso, Giorgio & Kartalidis, Avraam & Panichelli, Luis & Pinedo, Irene, 2011. "Methods and tools to evaluate the availability of renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1182-1200, February.
    2. Manwell, J.F. & Elkinton, C.N. & Rogers, A.L. & McGowan, J.G., 2007. "Review of design conditions applicable to offshore wind energy systems in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(2), pages 210-234, February.
    3. Gass, V. & Strauss, F. & Schmidt, J. & Schmid, E., 2011. "Assessing the effect of wind power uncertainty on profitability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2677-2683, August.
    4. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1556-1566, April.
    5. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Bueno, C. & Carta, J.A., 2006. "Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 312-340, August.
    8. Sailor, David J. & Smith, Michael & Hart, Melissa, 2008. "Climate change implications for wind power resources in the Northwest United States," Renewable Energy, Elsevier, vol. 33(11), pages 2393-2406.
    9. Carta, José A. & Velázquez, Sergio, 2011. "A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site," Energy, Elsevier, vol. 36(5), pages 2671-2685.
    10. 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.
    11. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    12. Breslow, Paul B. & Sailor, David J., 2002. "Vulnerability of wind power resources to climate change in the continental United States," Renewable Energy, Elsevier, vol. 27(4), pages 585-598.
    13. 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.
    14. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    15. López, P. & Velo, R. & Maseda, F., 2008. "Effect of direction on wind speed estimation in complex terrain using neural networks," Renewable Energy, Elsevier, vol. 33(10), pages 2266-2272.
    16. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    17. Pryor, S.C. & Barthelmie, R.J., 2010. "Climate change impacts on wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 430-437, January.
    18. Khadem, Shafiuzzaman Khan & Hussain, Muhtasham, 2006. "A pre-feasibility study of wind resources in Kutubdia Island, Bangladesh," Renewable Energy, Elsevier, vol. 31(14), pages 2329-2341.
    19. Khan, M.J. & Iqbal, M.T., 2004. "Wind energy resource map of Newfoundland," Renewable Energy, Elsevier, vol. 29(8), pages 1211-1221.
    20. Oliver Probst & Diego Cárdenas, 2010. "State of the Art and Trends in Wind Resource Assessment," Energies, MDPI, vol. 3(6), pages 1-55, June.
    21. 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.
    22. Oh, Ki-Yong & Kim, Ji-Young & Lee, Jae-Kyung & Ryu, Moo-Sung & Lee, Jun-Shin, 2012. "An assessment of wind energy potential at the demonstration offshore wind farm in Korea," Energy, Elsevier, vol. 46(1), pages 555-563.
    23. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary Islands," Applied Energy, Elsevier, vol. 88(11), pages 3869-3881.
    24. 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.
    25. Manwell, J.F. & Rogers, A.L. & McGowan, J.G. & Bailey, B.H., 2002. "An offshore wind resource assessment study for New England," Renewable Energy, Elsevier, vol. 27(2), pages 175-187.
    26. Jose Antonio Carta and Jaime Gonzalez, 2001. "Self-Sufficient Energy Supply for Isolated Communities: Wind-Diesel Systems in the Canary Islands," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 115-146.
    27. Abbes, Mohamed & Belhadj, Jamel, 2012. "Wind resource estimation and wind park design in El-Kef region, Tunisia," Energy, Elsevier, vol. 40(1), pages 348-357.
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