# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 4 Results and Discussions
For instance, the wind speed (m/s) and power density (W/m\({}^{2}\)) are in Figure 6 for seven megacities at various heights. Moreover, Figure 7 displays wind speed and power density for the chosen study sites. To note, the power density is calculated by the polynomial model. Similar to GWA, ERA5 also proves Sylhet as a weaker region than the other cities. The average height of 100 m has a wind speed of 5 m/s that provides a power output about 200 W/m\({}^{2}\). These reference data can be calculated to develop a statistical model for both power estimation and annual energy production. For given datasets, exponential and polynomial (order = 3) models fit the wind-power curve well, which are eventually compared with the physical model. While modeling, data sensitivity is observed, i.e., summarized in Table 2. Though there are some outliers in power datasets, wind and power data are kept at about a sample size of 1100 for general applications. Based on the determination of coefficients, residual standard errors (RSE), and sum of square error (SSE), the final simple power predictive models (SPPM) are established. Table 3 summarizes  
Figure 5: Time series of atmospheric boundary layer height (m) for the southern (left panels) and northern regions (right panels) averaged from 1979 to 2018 with a climatic base period of 1979 to 2000.  
the concluded forms of the SPPM with their accuracy, i.e., mean absolute percentage error (MAPE) and residual standard error (RSE). The developed SPPM incorporates only wind speed information regardless of any height or roughness and forecasts power density so that the models can generalize forecasting for any city in Bangladesh. Therefore, SPPM simplifies physical complexities and saves computational time and resources. The average determination of coefficients is around 0.90 for SPPM, which stands statistically strong. Based on the MAPE in Table 3, the polynomial (PN) and exponential (EXP) models indicate similar accuracy of 82%, whereas the linear model shows an MAPE of 58%. Thus, there are no further illustrations or investigations for the Linear model. Though the physical (PS) model shows an accuracy of 55%, the investigations are continued for comparative understandings with statistical approaches. For comparing GWA power datasets, an average wind speed of ERA5 from 1979 to 2018 is incorporated to estimate power (shown in Figure 7a). Particularly, the outputs from PN demonstrated in Figure 7b show the highest potential in Barishal and lowest potential in Sylhet. To note, the MAPE for PN yielded the lowest during training, i.e., around 21.80%. Though Figure 6 also replicates similar characteristics to Figure 7, i.e., stronger wind speed in southern regions, onshore areas, mostly closer to rivers, could provide reasonable energy as well, i.e., around 150 W/m\({}^{2}\) at 4 to 5 m/s. Comparing Figures 6 and 7 also indicates that either onsite observations or high-resolution model datasets are inevitable for wind power generation. Except for the Sylhet region, both land and coastal regions are quite suitable for accommodating modern turbine technologies. Even though ERA5 data shows an average height, i.e., 100 m for the minimum threshold to initiate turbine rotation, higher resolution observations with finer  
Figure 6: From GWA representing average (**a**) wind speed (m/s) at various heights (m) and (**b**) power density (W/m\({}^{2}\)). Figure 7: Same as in Figure 6 but valid for ERA5 (**a**) average wind speed (1979 to 2018) and for Polynomial (**b**) power density. instruments can provide accurate information for wind speed. Therefore, these figures (Figures 6 and 7) encourage establishing observations for scrutinizing detailed and more accurate wind information before turbine installations. The SPPM results provide valuable inferences in energy approximations regardless of height dependency and/or physics complexities. For validating model estimations, the SPPM performances are associated with previous studies.