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Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment

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  • Rodriguez-Hernandez, O.
  • Jaramillo, O.A.
  • Andaverde, J.A.
  • del Río, J.A.

Abstract

Due the different possibilities for fit the Probability Density Functions adjustable to a wind speed data set, a best fit selection criterion is developed based on slope, intercept values and standard errors of Ordinary Linear Regression model calculated from the probabilistic model and experimental data. Uncertainty associated with measuring instruments is analyzed, and an interpretation is presented in terms of the electric power generated. In addition, a methodology is proposed to generate scenarios of energy production used in financial evaluations, which is possible since the wind speed data used retain its uncertainty. The relevant conclusions are that a sampling technique based on representative average wind speeds does not reproduce the original distribution of wind speed data set, since for the observed sample, the parameters of the fitted distributions vary depending on sampling time. Accordingly, assessments based on this sample technique leads to a resource underestimation.

Suggested Citation

  • Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:244-252
    DOI: 10.1016/j.renene.2012.06.004
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    4. 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.
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    4. Hernández-Escobedo, Q. & Saldaña-Flores, R. & Rodríguez-García, E.R. & Manzano-Agugliaro, F., 2014. "Wind energy resource in Northern Mexico," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 890-914.
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