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

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...

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Bibliographic Details
Published in:Renewable energy 2013-02, Vol.50, p.244-252, Article 244
Main Authors: Rodriguez-Hernandez, O., Jaramillo, O.A., Andaverde, J.A., del Río, J.A.
Format: Article
Language:English
Subjects:
Adjustable
Applied sciences
Assessments
Correlation
data collection
electric power
Energy
Exact sciences and technology
Fit
Ji-test
linear models
Linear-regression
Mathematical models
measuring devices
Natural energy
Probabilistic
Probabilistic methods
probabilistic models
Probability theory
regression analysis
renewable energy sources
Sampling
Statistics
Uncertainty
Wind speed
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Summary: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. ► A best fit selection criterion between probabilistic model and data is developed. ► Uncertainty of the measuring instrument is analyzed in resource assessment. ► A methodology to generate financial scenarios is proposed. ► Fitted parameter distributions vary depending on average rate time. ► Assessments based on 10 min averages lead to a resource underestimation.
ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2012.06.004