###### Abstract
Figure 1: Geographical distribution of the provinces within the top twenty in terms of installed wind power capacity in Turkey. When the literature about determining wind energy potential is examined, studies are generally carried out based on only one point [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. It is not very meaningful to make evaluations for large areas such as districts and provinces by using only one point. For example, [PERSON] et al. [34] determined the energy density of Ambam in Cameroon. [PERSON] and [PERSON] [35] studied the wind energy potential of the Maling Mountain in China at a height of 30 m. The wind speeds were not extrapolated to a certain height. [PERSON] et al. [39] determined the wind power density of the Kisii Region in Kenya using wind speed data obtained from four stations at a height of 10 m. The wind speeds were extrapolated to different heights from 20 m to 70 m. The wind power density of the case study's station was calculated in accordance with these wind speeds. [PERSON] and [PERSON] [36] calculated the wind energy potential of the Kirklareli University Kayali Campus at an altitude of 100 m. [PERSON] et al. [37] obtained the wind power density in the southeast of Romania near to Tulcea city. All used wind speeds obtained from only one station. [PERSON] and [PERSON] [38] assessed the wind energy potential of Germany using several data points under climate change at the wind turbine scale. They produced a wind speed map by using a Wind Speed-Wind Shear model (WSWS). Although the wind speed map was created in the study, the wind potential evaluations were made on a point basis. Since the studies were carried out using a point-based approach, it is not meaningful to use this for the power potential calculation of large areas. Wind speed measured at multiple points spread over the studied area is required to consistently calculate the wind power potential over large areas. With the help of geographic information system (GIS) functions, wind speed values at multiple points can be used as input data, and wind speed maps of a particular area can be created using interpolation methods. In this way, a basis is provided for studies in order to calculate the wind power potential in a specific area. [PERSON] and [PERSON] [40] created a wind speed map of the southern states of India based on 28 data points using the kriging interpolation method of GIS tools. The wind speed map was created in counters to illustrate the wind speed values. Since the produced map was black and white, it was not clearly understood which wind speed value the areas between the two counters had. [PERSON] et al. [41] produced a wind speed map of mainland China using the inverse distance weighting (IDW) interpolation method. The wind energy potential of the study area was calculated for suitable areas for wind farm installation by excluding restricted areas, similar to this study. [PERSON] et al. [42] adopted a multi-criteria decision support system to determine the suitable areas for wind turbine installation in the western region of Iran using GIS tools. They determined the wind potential of the suitable sites in the study area, similar to this study. There was no approach for determining the most appropriate spatial interpolation method in both three studies. The determination of the interpolation method is a significant step in wind speed map production. Each interpolation method has its own characteristics and gives different results. The main purpose of the study was to determine the total wind energy potential in the Balikesir Province by adopting an appropriate approach according to the legal regulations in Turkey. The wind speed map of the Balikesir Province at a height of 100 m was produced considering the most appropriate spatial interpolation method. Subsequently, the wind power potential of the Balikesir Province at a height of 100 m was calculated by excluding legally prohibited areas and those unsuitable for wind generation.