2026-04-29T11:01:06Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/108782025-09-16T00:05:33Zcom_10259_8983com_10259_5086com_10259_2604col_10259_8984
Martínez Gutiérrez, Samuel
04c36a3b-29d1-4736-a4b0-f392925647e2
600
Sarabia Ortiz, Daniel
736
600
0000-0001-7802-3542
Sarabia Peinador, Luis Antonio
534
600
0000-0002-5936-7929
Ruiz González, Rubén
735
600
0000-0001-7006-2395
Merino Gómez, Alejandro
733
600
0000-0002-8301-7195
2025-09-15T12:32:04Z
2025-09-15T12:32:04Z
2025-04
2227-7390
https://hdl.handle.net/10259/10878
10.3390/math13081238
2227-7390
In order to assess the potential and suitability of a location to deploy a wind farm, it is essential to have a model of the joint probability density function of the wind speed and direction, fV,Θ(v,θ). The angular–linear model is widely used to obtain the analytical expression of the joint density from the parametric estimation of the probability density functions of wind speed, fV(v), and wind direction, fΘ(θ). In previous studies, the parameters of the marginal distributions were obtained by fitting the wind measurements to the cumulative distribution function (CDF) using the least squares method and then calculating the probability density function (PDF). In this study, we propose to directly fit the probability density function and then calculate the cumulative distribution function. It is shown that it has both computational and goodness-of-fit advantages. In addition, previous studies have been expanded, analysing the effect of the number of intervals on which wind speed and direction ranges are divided. The new parameter fitting method is evaluated and compared with the original proposal in terms of goodness of fit, using the coefficient of determination R2 as an estimator both in the probability density function (R2pdf) and in the cumulative distribution function (R2cdf). The computational times required to estimate the parameters using both methods will also be compared. The new approach is faster, and the goodness of the fitting is satisfactory for both estimators: it produces a better R2pdf, without significantly affecting the R2cdf, in contrast to the initial one where the R2pdf is smaller.
The paper is part of the projects “Optimal Real-Time Management of the Power-to-H2-toPower cycle (OptiMaPH2P)”, TED2021-131220B-I00, funded by MCIN/AEI (Ministerio de Ciencia e
Innovación del Gobierno de España/Agencia Estatal de Investigación) and by the European Union
“NextGenerationEU”, “Optimal real-time management under uncertainty for digital twins (OptiDit)”,
PID2021-123654OB-C33, funded by MCIN (Ministerio de Ciencia e Innovación del Gobierno de
España) and by the European Union “FEDER”. This paper is also part of the Doctoral Thesis of Samuel
Martínez-Gutiérrez, funded with a pre-doctoral contract for University Teacher Training (FPU), call
2022, awarded by the MUNI of Spain (Ministerio de Universidades del Gobierno de España).
application/pdf
eng
MDPI
Mathematics. 2025, V. 13, n. 8, 1238
https://doi.org/10.3390/math13081238
Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Wind speed
Wind direction;
Joint probability distributions
Parameter estimation
Computation time
Viento-Medición
Winds-Measurement
A New Approach to Estimate the Parameters of the Joint Distribution of the Wind Speed and the Wind Direction, Modelled with the Angular–Linear Model
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Mathematics
13
8
1238
THUMBNAIL
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oai:riubu.ubu.es:10259/10878
2025-09-16 02:05:33.958
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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