A fuzzy decision-making network model for offshore wind turbine selection based on simulated annealing algorithm.

As global demand for renewable energy surges, offshore wind power has become an indispensable component in the transition towards a more sustainable energy landscape. Selecting appropriate wind turbines for offshore wind power facilities is a critical process influenced by various factors, including turbine parameters, environmental conditions, and economic indicators. Traditional decision-making methods often fall short in addressing the complexity and dynamic nature of real-world scenarios, relying heavily on expert experience. This study proposes a novel and scalable method for offshore wind turbine selection by leveraging a fuzzy decision-making network and a conditional probability table optimized through a simulated annealing algorithm. The method is structured into three key modules. It allows for learning from existing similar cases and transferring knowledge to the current selection task in the absence of expert opinion. It has proven its worth in a case study at the Rudong Wind Farm in Jiangsu Province, China. The results show that this method can account for the complexities inherent in turbine selection and serve as a more comprehensive and adaptable tool for decision-makers. This study contributes to the renewable energy sector by providing a novel, data-driven, and practical method for offshore wind turbine selection and other real-world related applications. • A novel integrated approach for offshore wind turbine selection is proposed. • The simulated annealing algorithm is used to achieve parametric optimization. • The new approach adeptly handles uncertain data without expert experience. • The efficacy of the new methodology is demonstrated in a real-world project. • Future directions like factor recalibration and cross-scenario validation are discussed. [ABSTRACT FROM AUTHOR]