Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electricity. In this study, a new hybrid approach integrating Interval Rough Numbers (IRNs) into Best-Worst Method (BWM) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) is introduced for multi-criteria intelligent decision support to choose the best offshore wind farm site in a Turkey’s coastal area. Four alternatives in the Aegean Sea are considered based on a range of criteria. The results show the viability of the proposed approach which yields Bozcaada as the appropriate site, when compared to and validated using the other multi-criteria decision-making techniques from the literature, including IRN based MABAC, WASPAS, and MAIRCA.

在过去的 20 年里，海上风电场的发展在世界范围内变得越来越重要。最关键的原因之一是海上风力涡轮机的平均速度高于陆上风力涡轮机，从而产生更多电力。在这项研究中，引入了一种新的混合方法，将区间粗略数 (IRN) 与最佳最差方法 (BWM) 和根据折衷解决方案的替代和排名测量 (MARCOS) 相结合，用于多标准智能决策支持，以选择最佳离岸方法。土耳其沿海地区的风电场。根据一系列标准考虑了爱琴海的四种替代方案。结果显示了所提出的方法的可行性，该方法将 Bozcaada 作为合适的站点，