Wind speed modeling in wind energy based on one year of SCADA data using statistical distribution functions
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Abstract
Wind speed is one of the basic meteorological parameters in the field of wind energy, as it directly affects the efficiency and output power of wind turbines. Wind speed distribution is most often modeled using statistical functions, in particular the probability density function (PDF) and the cumulative distribution function (CDF). In this paper, a comparison of several statistical distributions (Weibull, Rayleigh, gamma, normal and beta) was performed through the analysis of the empirical distribution of wind speed recorded over a one-year period. For all distributions, the root mean square error (RMSE) and the mean absolute error (MAE) between observed and modeled values were calculated, and the smallest values of these criteria were used to identify the model that most accurately describes the actual wind behavior at the observed location. The obtained results enable a more precise assessment of the wind speed distribution and the energy potential of the location, which is of key importance for optimizing the operation of wind energy systems.
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References
[1] W. Ji, R. Li, W. Xue, Z. Cao, H. Yang, Q. Ning, X. Hu, and G. Liao, "Evaluation of ERA5 wind parameter with in-situ data offshore China", PLoS ONE, Vol. 20 (5), p. e0317751, https://doi.org/10.1371/journal.pone.0317751, (2025)
[2] M. Ćalasan, S. Vujošević, and G. Krunić, "Towards enhanced photovoltaic modeling: New single diode model variants with nonlinear ideality factor dependence", Engineering Science and Technology, an International Journal, Vol. 65, p. 102037, https://doi.org/10.1016/j.jestch.2025.102037, (2025)
[3] E. Chiodo, B. Diban, G. Mazzanti, and F. De Angelis, "A review on wind speed extreme values modeling and Bayes estimation for wind power plant design and construction", Energies, Vol. 16 (14), p. 5456, https://doi.org/10.3390/en16145456, (2023)
[4] D. Erdoğan, K. Yiğit, and B. Acarkan, "Bir rüzgâr enerji santralinin güç kapasitesine bağlı olarak üretebileceği elektrik enerjisi potansiyelinin belirlenmesi – Artvin ili örneği [Determination of the electrical energy production potential of a wind power plant depending on the power capacity: A case study for province of Artvin]", Journal of the Institute of Science and Technology, Vol. 11 (2), pp. 998–1013, https://doi.org/10.21597/jist.852919, (2021)
[5] Z. R. Shu and M. Jesson, "Estimation of Weibull parameters for wind energy analysis across the UK", Journal of Renewable and Sustainable Energy, Vol. 13 (2), p. 023303, https://doi.org/10.1063/5.0038001, (2021)
[6] E. Dokur, S. Ceyhan, and M. Kurban, "Analysis of wind speed data using Finsler, Weibull, and Rayleigh distribution functions", Electrica, Vol. 22 (1), pp. 52–60, https://doi.org/10.5152/electrica.2021.21044, (2022)
[7] P. V. Sireesha and T. Sandhya, "Statistical analysis of wind power density using different mixture probability distribution functions in coastal region of Andhra Pradesh", Electrical Engineering, Vol. 106 (3), pp. 3061–3082, https://doi.org/10.1007/s00202-023-02104-x, (2024)
[8] G. Krunić and S. Vasković, "Wind power plants", Faculty of Production and Management Trebinje, Trebinje (Bosnia and Herzegovina), (2022)
[9] Z. Toor, H. M. Bahaidarah, M. E. Zayed, S. Rehman, V. Khanna, and S. M. Shalaby, "Experimental investigation and CFD modeling on rooftop vertical axis wind turbine for sustainable residential buildings: Development of core design optimization and advanced performance analysis", Energy and Buildings, Vol. 353, p. 116901, https://doi.org/10.1016/j.enbuild.2025.116901, (2025)
[10] Z. Wang and W. Liu, "Wind energy potential assessment based on wind speed, its direction and power data", Scientific Reports, Vol. 11, p. 16879, https://doi.org/10.1038/s41598-021-96376-7, (2021)
[11] S. I. Abood and J. Fuller, "Power system protection and relaying: Computer-aided design using SCADA technology", CRC Press, Boca Raton, FL (USA), https://doi.org/10.1201/9781003394389, (2024)
[12] I. Delgado and M. Fahim, "Wind turbine data analysis and LSTM-based prediction in SCADA system", Energies, Vol. 14 (1), p. 125, https://doi.org/10.3390/en14010125, (2021)
[13] L. Fortuna, G. Nunnari, and S. Nunnari, "Nonlinear modeling of solar radiation and wind speed time series", Springer, Cham (Switzerland), https://doi.org/10.1007/978-3-319-38764-2, (2016)
[14] IPZ Uniprojekt TERRA d.o.o., "Elaborat zaštite okoliša: Izmjena tehničkog rješenja za vjetroelektrane Rudine, općina Dubrovačko primorje, Dubrovačko-neretvanska županija – ocjena o potrebi procjene utjecaja na okoliš", Vjetroelektrana Rudine d.o.o., Zagreb (Croatia), (2020)
[15] J. A. Burgos-Peñaloza, A. A. Lambert-Arista, O. R. García-Cueto, N. Santillán-Soto, E. Valenzuela, and D. E. Flores-Jiménez, "Comparative analysis of estimated small wind energy using different probability distributions in a desert city in northwestern México", Energies, Vol. 17 (13), p. 3323, https://doi.org/10.3390/en17133323, (2024)
[16] P. A. Amaya-Martínez, A. J. Saavedra-Montes, and E. I. Arango-Zuluaga, "A statistical analysis of wind speed distribution models in the Aburrá Valley, Colombia", CT&F – Ciencia, Tecnología y Futuro, Vol. 5 (5), pp. 121–136, https://doi.org/10.29047/01225383.36, (2014)
[17] I. Gordon, "Continuous probability distributions – A guide for teachers (Years 11–12)", Education Services Australia, Melbourne (Australia), (2013)
[18] T. Akman, H. H. Sayan, and Y. Söyletmez, "Estimation of wind power probability density distribution functions parameters by using meta-heuristic algorithms", Gazi Journal of Engineering Sciences, Vol. 10 (2), pp. 329–346, https://doi.org/10.30855/gmbd.0705A12, (2024)
[19] O. Mykhailenko, N. Karabut, V. Doskoch, O. Burtseva, V. Kuznetsov, S. Tsvirkun, and H. Kolomits, "Modeling of wind speed distribution in urban environment for the application of wind energy potential estimation: Case study", TEM Journal, Vol. 14 (1), pp. 107–116, https://doi.org/10.18421/TEM141-10, (2025)
[20] M. Rahman and L. M. Pearson, "A note on estimating parameters in the two-parameter Weibull distribution", Journal of the Korean Data & Information Science Society, Vol. 14 (4), pp. 1091–1102, (2003)
[21] C. D. Lai, D. N. P. Murthy, and M. Xie, "Weibull distributions and their applications", in: H. Pham (Ed.), Springer Handbook of Engineering Statistics, Springer, London (England), pp. 63–78, https://doi.org/10.1007/978-1-84628-288-1_3, (2006)
[22] C. Walck, "Hand-book on statistical distributions for experimentalists", Internal Report SUF-PFY/96-01, University of Stockholm, Stockholm (Sweden), https://staff.fysik.su.se/~walck/suf9601.pdf, (2007)
[23] P. E. Johnson, "Gamma distribution", University of Kansas, Lawrence, KS (USA), https://pj.freefaculty.org/guides/stat/Distributions/DistributionWriteups/Gamma/Gamma-02.pdf, (2013)
[24] T. O. Hodson, "Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not", Geoscientific Model Development, Vol. 15 (14), pp. 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, (2022)