# Systematic Analysis of Wind Resources for Eolic Potential in Bangladesh
## 4 Results and Discussions
As [10] had observed wind speed at 50 m height for Kutubdia sites, their power outputs are based on Weibull Distribution. Incorporating these wind speeds, SPPM then applied to estimate power. These comparisons also infer how SPPM performances are compatible with other distribution functions. Figure 8 and Table 4 display the power forecasts and their correlation based on information in [10]. The correlation coefficient is found for three observation sites in the range from 88 to 93%, whereas PN again outperforms both the EXP and PS models with an MAPE in the range of 23 to 26%. The comparison for the SPPM with [10] indicates that the model results are as reliable as Weibull distribution or higher predictability than Weibull. Table 4 implies that when data is more diverse and the wind speed is higher, the forecasting accuracy also increases for SPPM. Additionally, Figure 8 shows the predicted monthly power for three sites in Kutubdia (wind at 50 m) for a year by the EXP, PN, and PS models. Related with wind speed, the observation (open circle) is well matched with the PN model forecasts. The prediction by each model is PN > EXP > PS, whereas their correlation (r) and MAPE are about 0.89 and 25%. The data in [10] shows the highest monthly wind speed and power during monsoons. Figure 8 also signifies the maximum power harnessed during monsoons. Similar to current results, another study found power predictions for operational wind farms in hybrid modeling [20]. In data-driven models, SPPM performs well in reducing power estimations errors despite of source of data. This fitting implies how SPPM particularly polynomial regression can also be calculated for generalized applications. However, the model consistency is also important for customizing and minimizing errors with operational purposes. Therefore, additional assessments are carried out for confirming SPPM and its general applications. For further testing the model stability, the SPPM is utilized to forecast power by a historical observed wind data. The given wind speed is for Patenga in Chittagong (CG) at 20 m, Bremerhaven in Germany at 35 m, and Hofa in Jordan at 24 m and similar magnitudes were found. Using geographical location, the ERA5 data are extracted at 975 hPa (approximately 20 m) by keeping reference for 1010 hPa. Figure 9a shows the power based on historical wind speeds from [6] in 1996, whereas Figure 9b illustrates the power from the ERA5 wind datasets for Chittagong by the PN, EXP, and PS models. The estimated power is then compared with observations from GWA power datasets based on corresponding to the given wind speeds. In historical wind speeds, SPPM forecasts conclude that power estimations are also valid with observed wind datasets i.e., particularly PN model very clearly estimates energy as close as to GWA observations (Shown in Figure 9a). Even though ERA5 datasets have quite similar patterns to the observed data, there is a clear underestimation in the wind speed during the late-monsoon period. This difference also resulted in an underestimation in the power forecasts for the SPPM (given in Figure 9b). Moreover, the PN model predicts power higher but closer to observations than the EXP and PS. Between the observed and the modeled data, the power density correlations (r) are 0.97 and 0.60, respectively, for historical and ERA5 wind speeds (given in Table 4). The SPPM model predictability is significantly improved and is found to be 7.69 and 10.26% for the PN and EXP models, respectively. The predictability for the PS model is only 50%, similar to previous results in [14]. The results from PS also infer when wind speeds are higher than 5 m/s, and the model performances are closer to 50% accuracy. This discrepancy suggests incorporating accurate observations for validating SPPM outputs and operational wind turbines. To note, the power density indicates a pattern similar to wind speed and a maximum during monsoons, which means that SPPM follows the wind intensity characteristics.