Version 1
: Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (19:11:17 CET)
How to cite:
Burgos-Peñaloza, J.A.; Lambert-Arista, A.A.; García-Cueto, O.R.; Santillán-Soto, N.; Valenzuela, E.; Flores-Jiménez, D.E. Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico. Preprints2024, 2024030915. https://doi.org/10.20944/preprints202403.0915.v1
Burgos-Peñaloza, J.A.; Lambert-Arista, A.A.; García-Cueto, O.R.; Santillán-Soto, N.; Valenzuela, E.; Flores-Jiménez, D.E. Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico. Preprints 2024, 2024030915. https://doi.org/10.20944/preprints202403.0915.v1
Burgos-Peñaloza, J.A.; Lambert-Arista, A.A.; García-Cueto, O.R.; Santillán-Soto, N.; Valenzuela, E.; Flores-Jiménez, D.E. Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico. Preprints2024, 2024030915. https://doi.org/10.20944/preprints202403.0915.v1
APA Style
Burgos-Peñaloza, J.A., Lambert-Arista, A.A., García-Cueto, O.R., Santillán-Soto, N., Valenzuela, E., & Flores-Jiménez, D.E. (2024). Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico. Preprints. https://doi.org/10.20944/preprints202403.0915.v1
Chicago/Turabian Style
Burgos-Peñaloza, J.A., Edgar Valenzuela and David E. Flores-Jiménez. 2024 "Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern Mexico" Preprints. https://doi.org/10.20944/preprints202403.0915.v1
Abstract
In this paper, four probability functions are compared with the purpose of establishing a
methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern
Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to
the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz.
From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds).
The estimation of the parameters characterizing the probability density functions was performed
using different methods; the statistical models were evaluated by the coefficient of determination and
the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the
mean square error, the mean absolute error, and the mean absolute percentage error. It was found
that Weibull with the energy pattern factor method and Gamma using the method of moments were
the probability density functions that best describe the statistical behavior of the wind speed and,
therefore, better estimate the energy generated, so it is concluded that the proposed methodology
will allow having greater confidence, both in the estimation of the wind speed and in the small wind
energy available for its use.
Keywords
Short-term wind variability; Probability density function; Small wind energy estimation
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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