![]() ![]() Additionally, as the fuzzy_DS method determines the optimal locations with different confident levels, this method can benefit decision-makers to determine the risks associated with selecting a specific site for constructing solar PV farms. Comparisons of the fuzzy_AHP and fuzzy_DS methods at 20 points with various solar radiation intensities and the number of dusty days parameters indicate that the fuzzy_DS method can more reliably determine the optimal PV farm locations. However, it is 18.56%, 16.70%, 16.32% according to 95%, 99% and 99.5% confident levels in the fuzzy_DS method, respectively. The results show that 32% of the case study is located at high and good suitability classes in the fuzzy_AHP method. ![]() Finally, southeast of Fars province in Iran as a region with high sunny hours in the year is selected, and the applicability of proposed methods is examined. However, the DS method identifies output in different confident levels. In the AHP method, the proper weight for each input parameter is generated utilizing a pairwise comparison matrix. To locate the suitable areas for PV farms, firstly, a fuzzy-based method is utilized to homogenize the input parameters, thereafter, the analytical hierarchy process (AHP) and Dempster-Shafer (DS) methods are independently used. This study proposes a novel framework to determine the optimal location for constructing solar photovoltaic (PV) farms. Considering environmental concerns regarding air pollution which is induced by burning fossil fuels to generate electrical power, utilizing solar energy as a green and sustainable energy source is of great interest.
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