###### Abstract  
The remarkable developments in renewable energy at the global scale have paved the way for a sustainable future and cleaner environment. As a fundamental element of this renewable energy revolution, wind energy has received tremendous attention worldwide. In order to harness maximal energy from a wind farm, one crucial decision is the selection of a turbine that is most compatible with the geographical and topographical characteristics of the location under consideration. This selection mechanism ideally considers multiple conflicting decision criteria--i.e., an improvement in one criterion negatively impacts one or more other criteria. Therefore, a tradeoff solution is desired in which the selection criteria are simultaneously optimized to the best achievable limits. Considering the above observations, this paper proposes a TOPSIS (the technique for order of preference by similarity to ideal solution)-based wind turbine selection approach. The problem is modeled as a multi-criteria decision problem while considering hub height, wind speed, percentages of zero and rated output, and annual energy production as the decision criteria. A case study is shown for data collected from two potential sites in northern and north-western Saudi Arabia. Fifteen turbines with rated capacities ranging from 1.5 to 3 MW from various manufacturers were evaluated. Results indicate that the TOPSIS approach was effective in identifying the best turbines for each site. Furthermore, the proposed approach helped in identifying the similarities in the behavior of turbines for the two sites. A comparison of the TOPSIS approach with other multi-criteria decision-making techniques proved the robustness of the method. multi-criteria decision-making; TOPSIS; wind energy; wind turbine selection 20202023 October 202023 September 202023 October 202023 October 202023 September 202023 October 202023 October 202023 September 202023 October 202023 October 202023 September 202023 October 202023 October 202023 October 202023 September  
turbines; plus, they require far lesser operational and maintenance costs [3]. Furthermore, wind is not restricted by national borders and therefore cannot be controlled by countries, as is the case with fossil fuels and other traditional sources of energy. The long operational age of 20 to 25 years of wind turbines also makes the utilization of wind energy an attractive choice [3]. The importance of wind energy for sustainable growth comes from the recent trends in wind energy usage. Statistics indicate that the generation of wind energy has seen almost exponential growth over the past two decades, as illustrated in Figure 1. The figure shows that global wind energy generation has increased from almost 17 GW in the year 2000 to over 651 GW in 2019 worldwide [4]. According to a recent report by the Global Wind Energy Council [4], an onshore capacity of 60.4 GW was added worldwide in 2019, indicating an increase of 19% compared to 2018. China still leads the world, with a total of 230 GW of onshore installed capacity [4]. They are followed by the US, which had total onshore installations of over 100 GW in 2019 [4]. Other countries with new installations in the top five are India (2.4 GW), Spain (2.3 GW), and Sweden (1.6 GW) [4]. Many other countries around the globe are striving to add more wind energy to their national grids [4]. In order to design an efficient wind farm that would generate the maximum possible energy, a crucial decision is to select the turbine type that is most suitable to the geographic and topographic conditions of the site. This decision problem is formally referred to as the turbine selection problem. Over the years, the turbine selection problem has received notable attention from researchers [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Like many computational and engineering problems, the turbine selection problem has two main elements--the problem model and the computational model.