References
- Ahmed, S. A. 2013. Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq. Int J Phys Sci 8:186–92. doi:https://doi.org/10.5897/IJPS12.697.
- Ahmed Shata, A. S., and R. Hanitsch. 2006. The potential of electricity generation on the east coast of Red Sea in Egypt. Renewable Energy 31 (10):1597–615. doi:https://doi.org/10.1016/j.renene.2005.09.026.
- Akpinar, E. K., and S. Akpinar. 2005. An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy Conversion and Management 46 (11–12):1848–67. doi:https://doi.org/10.1016/j.enconman.2004.08.012.
- Aukitino, T., M. G. M. Khan, and M. R. Ahmed. 2017. Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters. Energy Conversion and Management 151:641–60. doi:https://doi.org/10.1016/j.enconman.2017.09.027.
- Azad, A. K., M. G. Rasul, M. M. Alam, S. M. Ameer Uddin, and S. K. Mondal. 2014. Analysis of wind energy conversion system using Weibull distribution. Procedia Engineering 90:725–32. doi:https://doi.org/10.1016/j.proeng.2014.11.803.
- Azad, A. K., M. G. Rasul, R. Islam, and I. R. Shishir. 2015. Analysis of wind energy prospect for power generation by three Weibull distribution methods. Energy Procedia 75:722–27. doi:https://doi.org/10.1016/j.egypro.2015.07.499.
- Azad, K., M. Rasul, P. Halder, and J. Sutariya. 2019. Assessment of wind energy prospect by weibull distribution for prospective wind sites in Australia. Energy Procedia 160:348–55. doi:https://doi.org/10.1016/j.egypro.2019.02.167.
- Burton, T., N. Jenkins, D. Sharpe, and E. Bossanyi. 2011. Wind energy handbook. New York, NY: John Wiley & Sons, Ltd. doi:https://doi.org/10.1002/9781119992714
- Carneiro, T. C., S. P. Melo, P. C. M. Carvalho, and B. A. P. De S. 2016. Particle Swarm optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region. Renewable Energy 86:751–59. doi:https://doi.org/10.1016/j.renene.2015.08.060.
- Celeska, M., K. Najdenkoski, V. Stoilkov, A. Buchkovska, Z. Kokolanski, and V. Dimchev. 2015. Estimation of Weibull parameters from wind measurement data by comparison of statistical methods. Proc - EUROCON 2015: 10.1109/EUROCON.2015.7313684.
- Chang, T. P. 2011. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy 88 (1):272–82. doi:https://doi.org/10.1016/j.apenergy.2010.06.018.
- Chen, Y., H. Li, K. Jin, and Q. Song. 2013. Wind farm layout optimization using genetic algorithm with different hub height wind turbines. Energy Conversion and Management 70:56–65. doi:https://doi.org/10.1016/j.enconman.2013.02.007.
- Costa Rocha, P. A., R. C. De Sousa, C. F. De Andrade, and M. E. V. Da Silva. 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. doi:https://doi.org/10.1016/j.apenergy.2011.08.003.
- Ersoz, S., T. C. Akinci, H. S. Nogay, and G. Dogan. 2013. Determination of wind energy potential in Kirklareli-Turkey. International Journal of Green Energy 10 (1):103–16. doi:https://doi.org/10.1080/15435075.2011.641702.
- Genc, A., M. Erisoglu, A. Pekgor, G. Oturanc, A. Hepbasli, and K. Ulgen. 2005. Estimation of wind power potential using weibull distribution. Energy Sources 27 (9):809–22. doi:https://doi.org/10.1080/00908310490450647.
- Hemanth Kumar, M. B., S. Balasubramaniyan, S. Padmanaban, and J. B. Holm-Nielsen. 2019. Wind energy potential assessment by weibull parameter estimation using multiverse optimization method: A case study of Tirumala region in India. Energies 12. doi:https://doi.org/10.3390/en12112158.
- Hulio, Z. H., W. Jiang, and R. S. Techno. 2019. - Economic assessment of wind power potential of Hawke’s Bay using Weibull parameter: A review. Energy Strategy Reviews 26:100375. doi:https://doi.org/10.1016/j.esr.2019.100375.
- Idriss, A. I., R. A. Ahmed, A. I. Omar, R. K. Said, and T. C. Akinci. 2019. Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti. Eng Sci Technol an Int J 23 (1):65–70. doi:https://doi.org/10.1016/j.jestch.2019.06.004.
- Indhumathy, D., C. V. Seshaiah, and K. Sukkiramathi. 2014. Estimation of Weibull parameters for wind speed calculation at Kanyakumari in India. Int J Innov Res Sci 3:8340–45.
- IRENA. 2018. Renewable energy outlook: Egypt. Abu Dhabi: International Renewable Energy Agency.
- IRENA. 2019. Renewable energy statistics 2019. Abu Dhabi: International Renewable Energy Agency.
- Jiang, H., J. Wang, J. Wu, and W. Geng. 2017. Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions. Renewable and Sustainable Energy Reviews 69:1199–217. doi:https://doi.org/10.1016/j.rser.2016.11.241.
- Justus, C. G., W. R. Hargraves, A. Mikhail, and D. Graber. 1978. Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology 17 (3):350–53. doi:https://doi.org/10.1175/1520-0450.
- Katinas, V., M. Marčiukaitis, G. Gecevičius, and A. Markevičius. 2017. Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania. Renewable Energy 113:190–201. doi:https://doi.org/10.1016/j.renene.2017.05.071.
- Khahro, S. F., K. Tabbassum, A. M. Soomro, L. Dong, and X. Liao. 2014. Evaluation of wind power production prospective and Weibull parameter estimation methods for Babaurband, Sindh Pakistan. Energy Conversion and Management 78:956–67. doi:https://doi.org/10.1016/j.enconman.2013.06.062.
- Khalid Saeed, M., A. Salam, A. U. Rehman, and M. Abid Saeed. 2019. Comparison of six different methods of Weibull distribution for wind power assessment: A case study for a site in the Northern region of Pakistan. Sustainable Energy Technologies and Assessments 36:100541. doi:https://doi.org/10.1016/j.seta.2019.100541.
- Koca, M. B., M. B. Kilic, and Y. Şahin. 2020. Using genetic algorithms for estimating Weibull parameters with application to wind speed. An Int J Optim Control Theor Appl 10:137. doi:https://doi.org/10.11121/ijocta.01.2020.00741.
- Konak, A., D. W. Coit, and A. E. Smith. 2006. Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 91 (9):992–1007. doi:https://doi.org/10.1016/j.ress.2005.11.018.
- Mohammadi, K., O. Alavi, A. Mostafaeipour, N. Goudarzi, and M. Jalilvand. 2016. Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management 108:322–35. doi:https://doi.org/10.1016/j.enconman.2015.11.015.
- Mortensen, N. G., U. S. Said, and J. Badger Wind Atlas for Egypt. Proc. Third Middle East-North Africa Renew. Energy Conf., Cairo, Egypt. June 12-14, 2006.
- Narayanan, S., and S. Azarm. 1999. On improving multiobjective genetic algorithms for design optimization. Structural Optimization 18 (2–3):146–55. doi:https://doi.org/10.1007/bf01195989.
- NREA.YYYYa Feasibility study for a large wind farm at Gulf of El Zayt. New and Renewable Energy Authority, Cairo, Egypt: 2008.
- NREA.YYYYb Successful implementation of shutdown on demand (SOD) at Gabel El Zayt wind farms. New and Renewable Energy Authority, Cairo, Egypt: 2018.
- Ohunakin, O. S., O. M. Oyewola, and M. S. Adaramola. 2013. Economic analysis of wind energy conversion systems using levelized cost of electricity and present value cost methods in Nigeria. International Journal of Energy and Environmental Engineering 4 (1):2–9. doi:https://doi.org/10.1186/2251-6832-4-2.
- REN21. Renewables 2019 global status report. Paris: REN21 Secretariat: 2019.
- Rinne, H. 2008. The Weibull distribution: A handbook. Boca Raton, FL: CRC press.
- Saeed, M. A., Z. Ahmed, J. Yang, and W. Zhang. 2020. An optimal approach of wind power assessment using Chebyshev metric for determining the Weibull distribution parameters. Sustainable Energy Technologies and Assessments 37:100612. doi:https://doi.org/10.1016/j.seta.2019.100612.
- Saleh, H., A. Abou El-Azm Aly, and S. Abdel-Hady. 2012. Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm. Suez Gulf, Egypt. Energy 44:710–19. doi:https://doi.org/10.1016/j.energy.2012.05.021.
- Sedghi, M., S. K. Hannani, and M. Boroushaki. 2015. Estimation of weibull parameters for wind energy application in Iran’s cities. Wind and Structures 21 (2):203–21. doi:https://doi.org/10.12989/was.2015.21.2.203.
- Shaltout, M. L., Z. Yan, D. Palejiya, and D. Chen. 2015. Tradeoff analysis of energy harvesting and noise emission for distributed wind turbines. Sustainable Energy Technologies and Assessments 10:12–21. doi:https://doi.org/10.1016/j.seta.2015.01.005.
- Sivanandam, S. N., and S. N. Deepa. 2008. Introduction to genetic algorithms. Springer-Verlag Berlin Heidelberg. doi:https://doi.org/10.1007/978-3-540-73190-0.
- Tabassum, A., M. Premalatha, T. Abbasi, and S. A. Abbasi. 2014. Wind energy: Increasing deployment, rising environmental concerns. Renewable and Sustainable Energy Reviews 31:270–88. doi:https://doi.org/10.1016/j.rser.2013.11.019.
- Tizgui, I., F. El Guezar, H. Bouzahir, and B. Benaid. 2017. Comparison of methods in estimating Weibull parameters for wind energy applications. International Journal of Energy Sector Management 11 (4):650–63. doi:https://doi.org/10.1108/IJESM-06-2017-0002.
- Werapun, W., Y. Tirawanichakul, and J. Waewsak. 2015. Comparative study of five methods to estimate weibull parameters for wind speed on Phangan Island, Thailand. Energy Procedia 79:976–81. doi:https://doi.org/10.1016/j.egypro.2015.11.596.
- Wu, J., J. Wang, and D. Chi. 2013. Wind energy potential assessment for the site of inner Mongolia in China. Renewable and Sustainable Energy Reviews 21:215–28. doi:https://doi.org/10.1016/j.rser.2012.12.060.