Seasonal and Altitudinal Wind Energy Potential Assessment at Varying Heights: A Case Study of Inani Beach Using Stochastic Modeling Approaches
DOI:
https://doi.org/10.53799/k8y06q73Abstract
Wind energy has become a vital component of global renewable energy strategies, offering sustainable power across various geographical regions. Accurately assessing wind energy potential is essential for optimizing turbine placement and maximizing energy generation efficiency. This study analyzes wind energy potential at varying heights, from 40m to 200m, as wind patterns and energy generation capacity change with altitude. Wind speeds at 40m average 4.85 m/s, whereas at 200m, they increase to 8.02 m/s, highlighting the advantages of higher altitudes for improved energy capture. Wind energy potential is assessed using statistical models, such as the Weibull distribution, alongside estimation methods like Maximum Likelihood Estimation (MLE), Least Squares Method (LSM), Method of Moments (MOM), and Energy Pattern Factor Method (EPFM). The analysis demonstrates that wind speeds increase with height, with wind speeds reaching 8.02 m/s at 200m in summer, compared to 5.89 m/s at 40m. Seasonal variations show the highest wind speeds between June and August, peaking at 8.95 m/s at 200m. The study applies advanced statistical methods to enhance wind energy assessments, highlighting the advantages of higher altitudes for more consistent and efficient energy generation. It offers insights into optimizing turbine placement for improved wind energy production.References
[1] S. A. Singh, P. K. Gupta, M. Rajeshwari, and T. Janumala, "Detection of Stress Using Biosensors," Mater. Today, vol. 5, no. 10, pp. 21003–21010, 2021.
[2] L. Ferrari, G. Pasini, and U. Desideri, "Towards a power production from 100% renewables: The Italian situation," SSRN Electronic Journal, 2022.
[3] J. A. Guarienti, A. Kaufmann Almeida, A. Menegati Neto, A. R. de Oliveira Ferreira, J. P. Ottonelli, and I. Kaufmann de Almeida, "Performance analysis of numerical methods for determining Weibull distribution parameters applied to wind speed in Mato Grosso do Sul, Brazil," Sustainable Energy Technologies and Assessments, vol. 42, July 2020.
[4] M. A. Hannan, et al., "Wind energy conversions, controls, and applications: A review for sustainable technologies and directions," 2023.
[5] Moulin, A., et al., "Assessment of Wind Energy Potential Across Europe Using Weibull Distribution," Renewable Energy, vol. 145, pp. 1234–1245, 2020.
[6] López, J., et al., "Enhancing Wind Resource Assessment with Three-Parameter Weibull Distribution," Energy Reports, vol. 8, pp. 567–578, 2022.
[7] Jain, R., and Lee, S., "Impact of Turbine Design Improvements on Wind Energy Output," Journal of Wind Engineering, vol. 46, no. 3, pp. 345–356, 2022.
[8] Singh, A., and Ghosh, P., "Champernowne Distribution: A Novel Approach for Wind Speed Analysis," Applied Energy, vol. 284, pp. 116–125, 2021.
[9] Zhou, L., et al., "Comparative Analysis of Weibull, Champernowne, and Lognormal Distributions for Wind Energy Forecasting in Coastal China," Atmospheric Measurement Techniques, vol. 15, pp. 757–773, 2022.
[10] Sánchez, M., et al., "Hybrid Statistical Models Combining Weibull and Champernowne Distributions for Wind Forecasting," Energy Conversion and Management, vol. 254, pp. 112–121, 2023.
[11] Müller, T., et al., "Deep Learning Algorithms Combined with Weibull Distribution for Enhanced Wind Speed Prediction," Scientific Reports, vol. 14, no. 1, pp. 1–12, 2024.
[12] J. A. Carta, P. Ramírez, and S. Velázquez, “A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands,” Renewable Energy, vol. 28, no. 4, pp. 607–615, Apr. 2019.
[13] M. Hasan, P. Dey, S. Janefar, N. A. Salsabil, I. J. Khan, N.-U.-R. Chowdhury, A. Arefin, and O. Farrok, “A critical analysis of wind energy generation potential in different regions of Bangladesh,” Energy Reports, vol. 11, pp. 2152–2173, 2024.
[14] M. A. Alanazi, M. Aloraini, M. Islam, S. Alyahya, and S. Khan, “Wind energy assessment using Weibull distribution with different numerical estimation methods: A case study,” Emerging Science Journal, vol. 7, no. 6, Dec. 2023.
[15] Wiklund, J., et al., "Integrated Models Combining Statistical and Machine Learning Approaches for Wind Energy Forecasting," Renewable Energy, vol. 189, pp. 345–356, 2022.
[16] Bygrave, L., et al., "Real-Time Data Integration in Wind Energy Models for Dynamic Forecasting," Energy Systems, vol. 14, no. 2, pp. 123–134, 2023.
[17] Guevara, R., et al., "Integration of Remote Sensing Technologies in Wind Energy Assessments," Remote Sensing of Environment, vol. 276, pp. 112–123, 2023.
[18] Patel, S., et al., "Enhancing Wind Speed Measurements in Challenging Environments Using Remote Sensing," Journal of Atmospheric and Oceanic Technology, vol. 40, no. 5, pp. 789–798, 2023.
[19] Tiwari, A., and Verma, S., "Hybrid Renewable Energy Systems: Integrating Wind and Solar Power with Energy Storage," Renewable Energy, vol. 180, pp. 456–467, 2024.
[20] Thomas, L., et al., "Artificial Intelligence in Optimizing Wind Farm Operation and Maintenance," Applied Energy, vol. 284, pp. 116–125, 2021.
[21] Li, H., and Zhang, X., "Predicting Wind Energy Potential Using AI and Large Datasets from Multiple Wind Farms," Energy AI, vol. 5, pp. 112–123, 2024.
[22] H. R. Ghosh, "Wind speed Weibull distribution and wind energy potential of Chandpur, Bangladesh," Dhaka University Journal of Applied Science and Engineering, vol. 6, no. 2, pp. 99–108, 2022.
[23] M. Hasan, P. Dey, S. Janefar, N. A. Salsabil, I. J. Khan, N.-U.-R. Chowdhury, A. Arefin, and O. Farrok, “A critical analysis of wind energy generation potential in different regions of Bangladesh,” Energy Reports, vol. 11, pp. 2152–2173, 2024.
[24] P. E. R. Reyes, R. V. D. Giesen, and M. H. G. Savenije, “Seasonal variability in wind energy potential across Europe,” Renewable and Sustainable Energy Reviews, vol. 97, pp. 469-479, 2018.
[25] T. A. Patwary, D. H. Didane, S. Al-Alimi, Y. A. M. Saif, and B. Manshoor, "Assessment of wind power potentials in Chittagong, Bangladesh using Weibull distribution function," Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, vol. 105, no. 1, pp. 154–165, 2023.
[26] M. Mondal, D. H. Didane, A. H. I. Ali, and B. Manshoor, "Technical assessment of wind energy potentials in Bangladesh," Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, vol. 96, no. 2, pp. 10–21, 2022.
[27] T. Stathopoulos, et al., "Urban wind energy: Some views on potential and challenges," Journal of Wind Engineering and Industrial Aerodynamics, vol. 179, pp. 146–157, May 2018.
[28] K. D. Islam, T. Theppaya, F. Ali, J. Waewsak, T. Suepa, J. Taweekun, T. Titseesang, and K. Techato, "Wind energy analysis in the coastal region of Bangladesh," Energies, vol. 14, no. 18, pp. 5628, 2021.
[29] M. M. Ahmed, S. K. Das, M. A. Haydar, M. M. H. Bhuiyan, M. I. Ali, and D. Paul, "Study of natural radioactivity and radiological hazard of sand, sediment, and soil samples from Inani Beach, Cox’s Bazar, Bangladesh," J. Nucl. Part. Phys., vol. 4, no. 2, pp. 69-78, 2014.
[30] U. Dev, S. Ahmad, M. R. Uddin, M. R. Hazari, M. Kabiruzzaman, M. S. R. Zishan, and K. F. Ahmed, “Day ahead wind energy forecasting in two distinct locations of Bangladesh using deep learning models,” in Proc. 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), Feb. 2025, pp. 1–6.
[31] U. Dev, S. Ahmad, M. R. Uddin, R. Dhar, H. Mubarak, M. R. Hazari, S. Mekhilef, M. Seyedmahmoudian, A. Stojcevski, and O. Alshammari, “Short-term wind speed forecasting using different LSTM deep learning,” Innovations in Electrical and Electronics Engineering: Proceedings of ICEEE 2024, vol. 2, no. 2, p. 199, 2025.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 AIUB Journal of Science and Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the work non-commercially provided the original work and source is appropriately cited.
