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**Figure 10.** R-Square values for monthly parameter estimation per each technique (own elaboration).

**Figure 11.** RMSE values for monthly parameter estimation per each technique (own elaboration).

RMSE is also one of the most common metrics to compare the techniques for distribution fitting; the smaller the value of RMSE the better the performance of estimation. Figure 11 shows that RMSE calculated for estimations based on EGO is lower than others for most of the months. Table 6 shows estimated value of shape (*k*) and scale (*c*) parameters of the TPWD for annual data using four different techniques. Figure 12 represents the histogram of observed wind speed and the estimated TPWD obtained using four techniques for annual data. From both Table 6 and Figure 12, it can be concluded that there are no huge differences between the parameters estimated using the four different techniques. Table 6 also shows the performance of techniques based on two different metrics: RMSE and R2. From Table 6 and Figure 13, it can be seen that EGO provides the lowest RMSE and the highest R2. In other words, EGO has the best performance among other techniques for estimating the parameters of TWPD for annual data.

In order to evaluate the wind energy potential, it is critical to estimate the TPWD parameters. EGO was utilized to estimate the parameters of TPWD in this study. EGO findings were compared with findings from the GA, SA, and DE algorithms. The EGO parameter estimation for TPWD yielded more precise outcomes. According to R<sup>2</sup> and RMSE, the EGO is superior to other algorithms.


**Table 6.** Performance comparison and parameter estimation for yearly data.

When the Weibull is chosen as PDF, the average wind power density per square meter is calculated as shown below [87,88]:

$$P\_W = \frac{1}{2} \rho \overline{\mathbf{x}}^3 \frac{\Gamma(1 + \frac{3}{k})}{\left[\Gamma(1 + \frac{1}{k})\right]^3} \tag{14}$$

Based on the estimated parameters of the WD, wind power density can be calculated by using the Equation (14), where *x* is the average wind speed, *k* is the shape parameter of the WD, and *ρ* is the standard air density, which is assumed to be equal to 1.225 kg/m<sup>3</sup> [87].

**Figure 12.** Probability density function estimation and histogram of observed WS for annual data (own elaboration).

*k* was estimated as equal to 2.05 and *x* is calculated as 3.81. Using Equation (14), wind power density can be calculated and it is equal to 62.89 W/m2.

**Figure 13.** RMSE (**a**) and R<sup>2</sup> values (**b**) for parameter estimation using yearly data (own elaboration).

According to the *Small Wind Guidebook* provided by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Skystream 3.7 is a type of wind turbine that can be used in urban areas [89]. The Skystream 3.7 is a wind turbine that turns wind into usable electricity for homes and small businesses. For households and smaller

businesses, Skystream 3.7 may supply 40% or up to 90% of their total energy needs [88]. Taking this information into account, potential WE is calculated under the assumption of having Skystream 3.7 installed in Gda ´nsk city center.

WE is calculated as shown in Equation (15). To be able to calculate potential WE, information about swept area (SWA) and power coefficient (PC) of the wind tribune must be known [90].

$$P\_E = P\_W \ast P \mathbb{C} \ast SA \tag{15}$$

SWA and maximum PC of Skystream 3.7 are 10.87 m<sup>2</sup> and 0.4, respectively [90–92]. *PE* is calculated as 273.4457 kWh. Annually, *PE* is calculated as 3281 kWh. According to Rachuneo.pl [93], energy prices per kWh in Poland range between 0.69 PLN and 0.78 PLN. By using this information, approximate annual revenue could be calculated between 2263 PLN and 2559 PLN.
