# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 5 Conclusions and Future Directions  
The growing energy demands and environmental concerns require sustainable energy sources. Nationwide, wind energy is often neglected due to its low magnitude. The present study investigates ERA5 and GWA datasets to analyze variations in wind speeds for potential Eolic estimations. Climatological variabilities imply that the strongest regions are coastal belts such as Barishal, Khulina, and Chittagong, whereas Rajshahi and Rangpur in the northern regions are also found promising for harvesting wind energy. Though Dhaka and Mymensing demonstrate moderate strength, Sylhet is the weakest region for harvest of wind energy. Similarly, the mean boundary layer height shows a decreasing trend in Sylhet and Dhaka, while the other cities are relatively safer places from atmospheric turbulence for optimized height, i.e., about 100 m to incorporate modern technologies. This study also applied wind power-curve approximations, developed a simple power predictive model (SPPM), and compared with historical and GWA observations. During training, the determination of coefficients and mean absolute percentage (MAPE) are 0.90 and 22% for Exponential (EXP) and Polynomial (PN) models. The SPPM also predicted power from historical (Chitttagong) observations and ERA5 wind speed with an accuracy of 93 and 90% respectively for PN and EXP compared with GWA. Finally, the results also suggest that about 2200 and 1400 MW of energy are possible to harvest annually respectively from the \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Regions** & \multicolumn{3}{c}{**(a) Southern Region**} & \multicolumn{3}{c}{**(b) Northern Region**} \\ \hline MAPE (\%) & Barishal & Khulina & Chittagong & Dhaka & Rajshahi & Rangpur & Mymensingh & Sylhet \\ Polynomial & 2.56 & 4.78 & 1.56 & 4.53 & 6.12 & 16.22 & 4.21 & 6.8 \\ Exponential & 14.57 & 20.13 & 16.92 & 9.85 & 16.95 & 21.06 & 3.47 & 22.69 \\ Physical & 57.25 & 61.23 & 60.52 & 67.26 & 65.08 & 71.19 & 69.67 & 72.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SPPM prediction error (%) in annual energy production. Figure 10: Annual energy production for (**a**) southern Regions and (**b**) northern regions in Bangladesh by SPPM estimations, i.e., Polynomial (PN), Exponential (EXP), and Physical (PS) models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Regions** & \multicolumn{3}{c}{**(a) Southern Regions**} & \multicolumn{3}{c}{**(b) Northern Regions**} \\ \hline Stats & Barishal & Khulina & Chittagong & Dhaka & Rajshahi & Rangpur & Mymensingh & Sylhet \\ Minimum & 0.44 & 0.79 & 0.81 & 0.41 & 0.50 & 0.73 & 0.51 & 0.68 \\ Median & 5.13 & 4.96 & 4.79 & 3.74 & 4.31 & 3.85 & 3.44 & 2.78 \\ Mean & 5.56 & 5.33 & 5.19 & 4.06 & 4.58 & 4.07 & 3.66 & 2.96 \\ Mode & 5.20 & 4.38 & 2.55 & 5.26 & 1.52 & 3.85 & 3.34 & 2.26 \\ Maximum & 16.58 & 13.99 & 13.75 & 10.42 & 13.44 & 11.16 & 9.99 & 7.61 \\ Standard Deviation & 2.73 & 2.43 & 2.32 & 1.88 & 2.32 & 1.76 & 1.72 & 1.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: For hourly-wind speed (m/s): Descriptive statistics from ERA5 datasets in 2018.  
southern (Barishal) and northern (Rajshahi) regions. For annual production, an MAPE is obtained at 5.85 and 15.71%, respectively, for PN and EXP, outperforming PS, i.e., 64.6%, which suggests applications with suitable wind turbines. These findings will improve in applications to wind energy for implementing advanced technology. For operational plants, SPPM is beneficial for forecasting energy at small-scale in remote areas, agricultural lands, and/or building rooftops. For SPPM applications in operational purposes, high resolution observations will enhance the model robustness as a mobile-app or smart forecast meter. The research targets to study comparing SPPM forecasts with observed wind and power projecting in the future. Conceptualization, M.H. and M.H. ; methodology, M.H. ; software, M.H., S.K.P. ; formal analysis, M.H. ; investigation, M.H., S.K.P. ; resources, M.H. ; data curation, M.H., S.K.P.