Solar energy as a clean, affordable, and sustainable resource to generate electrical power is of interest and preferred in arid and semi-arid regions. However, determination of the optimal location of a solar power farm to avail the maximum potential of solar energy is a big challenge. In order to obtain this goal, the geographical information system (GIS) techniques along with a wide range of computational methods and decision-making strategies can be used in order for the optimal location of these solar farms to be determined efficiently through spatial information of the targeted areas ^1,2,3. One of the straightforward strategies is to combine fuzzy and Dempster-Shafer (DS) methods for specifying an output in different confident levels. This combinational method, named fuzzy_DS, can benefit from decision-makers to determine the risks associated with selecting a specific site for constructing solar photovoltaic (PV) farms. For this purpose, the southeast of Fars province in Iran, located between latitudes 28° 49΄and 29° 24΄ N and longitudes 54° 02΄ and 54° 45΄ E, as a region of case study with high sunny hours in the year is selected, and the applicability of proposed method is tested. Moreover, eleven parameters including solar radiation intensity, air temperature, distance to PTL, distance to major roads, distance to residential areas, land elevation, land slope, land use, number of cloudy days, relative humidity, and number of dusty days are considered as input parameters. The results show that 18.56%, 16.70%, 16.32% of the case study area are located at high and good suitability classes according to 95%, 99% and 99.5% confident levels in the fuzzy_DS method, respectively. The below figure illustrates the results obtained through the fuzzy_DS method with confidence levels of 95% and 99%. 

(a)

(b)

The fuzzy_DS method results with different confidence levels of (a) 95%, and (b) 99%.

As it is obvious from the above figure, with increasing the confidence level, the area of regions with a high suitability index decreases. As seen, the southern parts are found to be suitable in all confidence levels. Finally, in this research, the results of fuzzy_DS method compared with the fuzzy_analytical hierarchy process (fuzzy_AHP) method. According to the next figure, the fuzzy_AHP model determines around 36% of the area as unsuitable regions, whereas the fuzzy_DS method categorized the majority of the area (as 60%) as low and moderately suitable in all confidence levels. It is noticeable that after increasing the confidence level, the fraction of unsuitable areas increases. The fuzzy_DS model is able to generate the final map based on the target level of confidence, as a straightforward feature, which cannot be obtained by the fuzzy_AHP method.

The fuzzy_AHP method results.

Further information can be found at the main article.

References

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2. Pili S, Desogus G, Melis D. A GIS tool for the calculation of solar irradiation on buildings at the urban scale, based on Italian standards. Energy Build. 2018;158:629-646. doi:10.1016/J.ENBUILD.2017.10.027

3. Machete R, Falcão AP, Gomes MG, Rodrigues AM. The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential. Energy Build. August 2018. doi:10.1016/J.ENBUILD.2018.07.064