**4. Data**

The hourly WS dataset for Gda ´nsk (latitude: 54.352◦ N, longitude: 18.646◦ E, 10 m. height) over the last seven years between 1 January 2015 and 26 July 2021 was gathered from Open Weather Map [82]. Then, using MLE, the TPWD was fitted to the WS data for each month and the annual data. For the purpose of determining the optimal parameters of the WD that maximize the likelihood function, the SA, GA, DE, and EGO were applied and performance of the techniques was compared. Following the obtaining of the parameters, potential WE and wind power for Gda ´nsk, the capital of the Pomerania Voivodeship in Poland, were calculated.

The dataset contains 60,100 rows and 25 columns and it provides information about WS, minimum temperature, maximum temperature, pressure, wind angle, amount of rain, amount of snow, information about how the weather looks (rainy, snowy, etc.).

Table 3 shows summary statistics regarding monthly average of WS and minimum and maximum temperatures in Celcius<sup>º</sup>. As shown in the table, the warmest month for Gda ´nsk is August and the coldest month is January. Table 3 also presents the monthly average of WS (m/s) in Gda ´nsk. As shown in the table, the monthly average WS does not differ dramatically between months within a year. According to the table, it can be concluded that the months in which the average WS is higher than others are April, December, and May. The lowest WS average is observed in August.


**Table 3.** Average WS, minimum temperature, maximum temperature per month in Gda ´nsk between 1 January 2015 and 26 July 2021.

Since obtaining the dataset from the third-party vendor was easy and quick and since the dataset contains hourly WS information for almost seven years, it was preferred to be used in the study. However, variables, including WS, in the dataset were collected by a single sensor located near the old town in Gda ´nsk. In conclusion to this, estimations for potential wind power were made only for this location. There was also no possibility to ge<sup>t</sup> WS information for different heights or for different parts of the city (e.g., parts of the city where long and tall buildings are located). For future studies, researchers plan to obtain datasets from different sources, such as local authorities or any other official sources, to be able to avoid the limitations mentioned above.
