Solar energy, as a major and least-cost renewable resource, has attracted extensive attention of experts and scholars. However, the establishment of the power station is time-consuming and costly. And once selected, it is difficult to change. So it is crucial to choose the appropriate site of power station. This paper combines data analysis with multi-criteria group decision-making to solve this problem. First of all, K-means clustering method is selected to process the data according to the characteristics of the data. Secondly, the results obtained by K-means method are represented by probabilistic linguistic term sets. Thirdly, Bonferroni Mean operator is used to adjust the weight of the criterion, which considers the consensus among experts. Fourthly, Technique for Order Preference by Similarity to Ideal Solution method is employed to rank the alternatives and select the best one. Finally, sensitivity analysis, comparison analysis and simulation are carried out to further confirm the robustness and advantage of the model. This model can help decision makers to better understand the basic situation of power station sites, make the right decisions, and improve some candidate sites according to the results.
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