Abstract
Accurate estimation of wind distribution plays a vital role in modelling more efficiently and minimizing theoretical computation errors in terms of practical applications. This research strives to conduct elaborately an assessment of the wind behavior and features of an international airport based on various heights from the ground level. Wind speed at each height, composed of 10 m, 20 m, 30 m, 40 m, and 50 m, is computed by applying the power-law equation. After that, the wind profile at each height is comparatively estimated based on the graphical, empirical, and maximum-likelihood method. Several statistical tools, namely, root mean square (RMSE) and R-squared (R2), are performed to make a fair comparison among the methods that help estimate the Weibull parameters. The results of this study reveal that the empirical and maximum-likelihood methods exhibit better performance than the graphical method in estimating the shape and scale parameter, irrespective of heights. Moreover, the findings of the study imply that the airport has a moderate potential to make use of generating electricity and hydrogen from wind power.
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References
Bagci, K., Arslan, T., & Celik, H. E. (2021). Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey. Renewable and Sustainable Energy Reviews, 135, 110110.
Bañuelos-Ruedas, F., Angeles-Camacho, C., & Rios-Marcuello, S. (2010). Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights. Renewable and Sustainable Energy Reviews, 14(8), 2383–2391.
Boopathi, K., Kushwaha, R., Balaraman, K., Bastin, J., Kanagavel, P., & Reddy Prasad, D. M. (2021). Assessment of wind power potential in the coastal region of Tamil Nadu, India. Ocean Engineering, 219, 108356.
Chen, H., Birkelund, Y., Anfinsen, S. N., Staupe-Delgado, R., & Yuan, F. (2021). Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic. Scientific Reports, 11(1), 7613.
Costa Rocha, P. A., de Sousa, R. C., de Andrade, C. F., & Da Silva, M. E. V. (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy, 89(1), 395–400.
Deaves, D. M., & Lines, I. G. (1997). On the fitting of low mean windspeed data to the Weibull distribution. Journal of Wind Engineering and Industrial Aerodynamics, 66(3), 169–178.
Deep, S., Sarkar, A., Ghawat, M., & Rajak, M. K. (2020). Estimation of the wind energy potential for coastal locations in India using the Weibull model. Renewable Energy, 161, 319–339.
Đurišić, Ž., & Mikulović, J. (2012). A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP. Renewable Energy, 41, 407–411.
El Khchine, Y., & Sriti, M. (2021). Performance evaluation of wind turbines for energy production in Morocco’s coastal regions. Results in Engineering, 10, 100215.
Gualtieri, G., & Secci, S. (2012). Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison. Renewable Energy, 43, 183–200.
Jung, C., & Schindler, D. (2021). The role of the power law exponent in wind energy assessment: A global analysis. International Journal of Energy Research, 45, 8484–8496.
Justus, C. G., & Mikhail, A. (1976). Height variation of wind speed and wind distributions statistics. Geophysical Research Letters, 3(5), 261–264.
Kim, D.-Y., Kim, Y.-H., & Kim, B.-S. (2021). Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear. Energy, 214, 119051.
Mahesh, K. (2021). A statistical analysis and artificial neural network behavior on wind speed prediction: Case study. Theory and Practice of Mathematics and Computer Science, 6, 38–56.
Ohunakin, O. S., Adaramola, M. S., & Oyewola, O. M. (2011). Wind energy evaluation for electricity generation using WECS in seven selected locations in Nigeria. Applied Energy, 88(9), 3197–3206.
Saeed, M. A., Ahmed, Z., Hussain, S., & Zhang, W. (2021). Wind resource assessment and economic analysis for wind energy development in Pakistan. Sustainable Energy Technologies and Assessments, 44, 101068.
Suzer, A. E., Atasoy, V. E., & Ekici, S. (2021). Developing a holistic simulation approach for parametric techno-economic analysis of wind energy. Energy Policy, 149, 112105.
Tonsie Djiela, R. H., Tiam Kapen, P., & Tchuen, G. (2020). Wind energy of Cameroon by determining Weibull parameters: Potential of a environmentally friendly energy. International Journal of Environmental Science and Technology, 18, 2251–2270.
Wadi, M., & Elmasry, W. (2021). Statistical analysis of wind energy potential using different estimation methods for Weibull parameters: A case study. Electrical Engineering. https://doi.org/10.1007/s00202-021-01254-0
Wang, L., Liu, J., & Qian, F. (2021). Wind speed frequency distribution modeling and wind energy resource assessment based on polynomial regression model. International Journal of Electrical Power & Energy Systems, 130, 106964.
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Tatli, A., Suzer, A.E., Filik, T., Karakoc, T.H. (2024). A Case Study on Investigating Probabilistic Characteristics of Wind Speed Data for Green Airport. In: Karakoc, T.H., Rohács, J., Rohács, D., Ekici, S., Dalkiran, A., Kale, U. (eds) Solutions for Maintenance Repair and Overhaul. ISATECH 2021. Sustainable Aviation. Springer, Cham. https://doi.org/10.1007/978-3-031-38446-2_30
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DOI: https://doi.org/10.1007/978-3-031-38446-2_30
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