Assessment and Characterization of Potential Locations for Wind Energy Harvest in Bangladesh

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dc.contributor.author Tasnim, Maliha
dc.contributor.author Rifa, Tasnia Islam
dc.date.accessioned 2024-01-03T08:58:20Z
dc.date.available 2024-01-03T08:58:20Z
dc.date.issued 2023-05-30
dc.identifier.citation [1] Mondal, M. A. H., & Denich, M. (2010). Assessment of renewable energy resources potential for electricity generation in Bangladesh. Renewable and Sustainable Energy Reviews, 14(8), 2401-2413. [2] Khan, M. J., Iqbal, M. T., & Mahboob, S. (2004). A wind map of Bangladesh. Renewable energy, 29(5), 643-660. [3] Khadem, S. K., & Hussain, M. (2006). A pre-feasibility study of wind resources in Kutubdia Island, Bangladesh. Renewable energy, 31(14), 2329-2341. [4] Azad, A. K., Rasul, M. G., Alam, M. M., Uddin, S. A., & Mondal, S. K. (2014). Analysis of wind energy conversion system using Weibull distribution. Procedia Engineering, 90, 725-732. [5] Dastagir, M. R. (2015). Modeling recent climate change induced extreme events in Bangladesh: A review. Weather and Climate Extremes, 7, 49-60. [6] Watts, D., & Jara, D. (2011). Statistical analysis of wind energy in Chile. Renewable Energy, 36(5), 1603-1613. [7] Cadenas, E., & Rivera, W. (2007). Wind speed forecasting in the south coast of Oaxaca, Mexico. Renewable energy, 32(12), 2116-2128. [8] Fyrippis, I., Axaopoulos, P. J., & Panayiotou, G. (2010). Wind energy potential assessment in Naxos Island, Greece. Applied Energy, 87(2), 577-586. [9] Næss, A., & Gaidai, O. (2009). Estimation of extreme values from sampled time series. Structural safety, 31(4), 325-334. [10] Alam, M. M., & Azad, A. K. (2009, December). Analysis of Weibull Parameters for the Three Most Prospective Wind Sites of Bangladesh. In Proceedings of the International Conference on Mechanical Engineering, Dhaka, Bangladesh, ICME09-FM-07. [11] Harris, R. I. (2001). The accuracy of design values predicted from extreme value analysis. Journal of wind engineering and industrial aerodynamics, 89(2), 153-164. [12] Ren, G., Wan, J., Liu, J., & Yu, D. (2019). Characterization of wind resource in China from a new perspective. Energy, 167, 994-1010. 91 [13] Azad, A. K., Rasul, M. G., Islam, R., & Shishir, I. R. (2015). Analysis of wind energy prospect for power generation by three Weibull distribution methods. Energy Procedia, 75, 722-727. [14] Shi, J., Guo, J., & Zheng, S. (2012). Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renewable and Sustainable Energy Reviews, 16(5), 3471-3480. [15] Islam, A., Islam, M. S., Hasan, M., & Khan, A. H. (2014). Analysis of Wind Characteristics and Wind Energy Potential in Coastal Area of Bangladesh: Case Study-Cox’s Bazar. world, 21(26,998), 32-446. [16] Wahid, C. M. M., Rahman, F. M., Wohiduzzaman, K., Rima, N. A., & Honi, F. D. (2014, May). Design of a wind farm in the coastal island sandwip, Bangladesh. In 2014 3rd International Conference on the Developments in Renewable Energy Technology (ICDRET) (pp. 1-4). IEEE. [17] Hasan, K., & Fatima, K. (2011, June). Feasibility of hybrid power generation over wind and solar standalone system. In 2011 5th International Power Engineering and Optimization Conference (pp. 139-143). IEEE. [18] R., & Modarres, R. (2008). Extreme value frequency analysis of wind data from Isfahan, Iran. Journal of wind Engineering and industrial Aerodynamics, 96(1), 78-82. [19] Lombardo, F. T., Main, J. A., & Simiu, E. (2009). Automated extraction and classification of thunderstorm and non-thunderstorm wind data for extreme-value analysis. Journal of Wind Engineering and Industrial Aerodynamics, 97(3-4), 120-131. [20] Ding, J., & Chen, X. (2014). Assessment of methods for extreme value analysis of non Gaussian wind effects with short-term time history samples. Engineering structures, 80, 75-88. [21] Larsén, X. G., Kalogeri, C., Galanis, G., & Kallos, G. (2015). A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications. Renewable Energy, 80, 205-218. [22] de Zea Bermudez, P., & Kotz, S. (2010). Parameter estimation of the generalized Pareto distribution—Part I. Journal of Statistical Planning and Inference, 140(6), 1353-1373. [23] Di Bucchianico, A. (2008). Coefficient of determination (R 2). Encyclopedia of statistics in quality and reliability. 92 [24] Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018, December). A comparison of ARIMA and LSTM in forecasting time series. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 1394-1401). IEEE. [25] Smyl, S. (2020). A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting, 36(1), 75-85. [26] Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., & Chen, R. C. (2023). Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics-Simulation and Computation, 52(2), 279-290. [27] Maleki, A., Nasseri, S., Aminabad, M. S., & Hadi, M. (2018). Comparison of ARIMA and NNAR models for forecasting water treatment plant’s influent characteristics. KSCE Journal of Civil Engineering, 22, 3233-3245. [28] Kalogeri, C., Galanis, G., Spyrou, C., Diamantis, D., Baladima, F., Koukoula, M., & Kallos, G. (2017). Assessing the European offshore wind and wave energy resource for combined exploitation. Renewable energy, 101, 244-264. [29] Ahmad, S. S., Al Rashid, A., Raza, S. A., Zaidi, A. A., Khan, S. Z., & Koç, M. (2022). Feasibility analysis of wind energy potential along the coastline of Pakistan. Ain Shams Engineering Journal, 13(1), 101542. [30] Couto, A., & Estanqueiro, A. (2021). Assessment of wind and solar PV local complementarity for the hybridization of the wind power plants installed in Portugal. Journal of Cleaner Production, 319, 128728. [31] Extreme Value Analysis (EVA) in Python, https://georgebv.github.io/pyextremes/ [32] Asia-Pacific Data-Research Center (APDRC), http://apdrc.soest.hawaii.edu/ [33] Soukissian, T. H., & Tsalis, C. (2015). The effect of the generalized extreme value distribution parameter estimation methods in extreme wind speed prediction. Natural Hazards, 78, 1777-1809. [34] Saeidi, Davood, M. Mirhosseini, Ahmad Sedaghat, and Ali Mostafaeipour. "Feasibility study of wind energy potential in two provinces of Iran: North and South Khorasan." Renewable and Sustainable Energy Reviews 15, no. 8 (2011): 3558-3569. [35] Holmes, J. D., & Moriarty, W. W. (1999). Application of the generalized Pareto distribution to extreme value analysis in wind engineering. Journal of Wind Engineering and Industrial Aerodynamics, 83(1-3), 1-10. 93 [36] Wang, H. Z., Li, G. Q., Wang, G. B., Peng, J. C., Jiang, H., & Liu, Y. T. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied energy, 188, 56-70. [37] Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2012). Current methods and advances in forecasting of wind power generation. Renewable energy, 37(1), 1-8. [38] Wang, X., Guo, P., & Huang, X. (2011). A review of wind power forecasting models. Energy procedia, 12, 770-778. [39] Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., & Yan, Z. (2009). A review on the forecasting of wind speed and generated power. Renewable and sustainable energy reviews, 13(4), 915-920. [40] Erdem, E., & Shi, J. (2011). ARMA based approaches for forecasting the tuple of wind speed and direction. Applied Energy, 88(4), 1405-1414. [41] Lazić, L., Pejanović, G., & Živković, M. (2010). Wind forecasts for wind power generation using the Eta model. Renewable Energy, 35(6), 1236-1243 en_US
dc.identifier.uri http://hdl.handle.net/123456789/2013
dc.description Supervised By Prof. Dr. Mohammad Ahsan Habib, Co-Supervised By Mr. Tanvir Shahriar, Assistant Professor, Department of Production and Mechanical Engineering(MPE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh en_US
dc.description.abstract Wind, solar, hydro, and geothermal renewable energy sources are becoming more and more important for tackling the energy issue and reducing climate change. Wind energy is regarded as one of nature's cleanest, safest, and most durable sources of energy among them. Additionally, plentiful and widely dispersed, wind energy is a desirable choice for power generation. Energy consumption is rising quickly in Bangladesh as a result of population increase and economic expansion. Due to the nation's low indigenous energy resources, imports account for the majority of its energy requirements. The article focuses on locating prospective wind energy harvesting sites in Bangladesh and investigating applications for it. To estimate the prospective sites, two extreme value distribution models—Generalized Extreme Value (GEVD) and Generalized Pareto Distribution (GPD)—have been applied. These distributions make it easier to simulate the behavior of extreme situations like high wind speeds, which are crucial for the production of wind energy. For the chosen sites, a wind rose diagram has also been used to study the directional dispersion of the wind. In order to construct wind turbines that harvest the most energy possible, it is critical to display the frequency and direction of the wind for each place in this graphic. In addition, the study has employed machine learning (ML) methods and statistical models to forecast wind speed behavior. In order to maximize the energy production of wind farms, it is essential to improve the design, operation, and maintenance of wind turbines. Using wind energy technology offers enormous potential for supplying the energy needs of emerging nations like Bangladesh. The study offers crucial insights into locating possible wind energy production sites and investigating their effective use. en_US
dc.language.iso en en_US
dc.publisher Department of Mechanical and Production Engineering(MPE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh en_US
dc.subject Renewable Energy, Extreme Value Analysis (EVA), GPD, GEVD, Wind Rose, Forecasting. en_US
dc.title Assessment and Characterization of Potential Locations for Wind Energy Harvest in Bangladesh en_US
dc.type Thesis en_US


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