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As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal's marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN- Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. "ACO-TCN-Attention" is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy. [ABSTRACT FROM AUTHOR]

A numerical model is developed to compare the performances of mooring materials for a gravity cage system. At first, a small-scale model is built and it is validated by the previous experiments. With the same method, a full-scale gravity cage model is built to study the performance of different mooring materials. Link elements are used to model the wire ropes to consider the weight, and nonlinear spring elements are used to model the composite fibre ropes to consider the nonlinear stiffness. High-tenacity polyester multifilament fibre has the best performance. It can reduce the average value and amplitude of the mooring forces, which also contributes to reducing the stress concentration on the collar. At last, the mooring forces at specific locations in Portugal are calculated and the distributions of mooring force amplitudes are analysed. • A numerical model is developed to compare the performances of mooring materials for a gravity cage system. • A small-scale model is built and it is validated by the previous experiments. • With the same method, a full-scale gravity cage model is built to study the performance of different mooring materials. • Wire ropes and composite fibre ropes are studied and are modelled considering the nonlinear stiffness. • High-tenacity polyester multifilament fibre has the best performance as it can reduce the mooring forces. [ABSTRACT FROM AUTHOR]