Artificial Intelligence approaches to optimizing energy efficiency in marine renewable energy with a focus on tidal power

This review adopts the use of artificial intelligence based prediction models in enhancing energy efficiency in marine energy generation using tidal power. Traditional forecasting techniques such as hydrodynamic simulation and statistical modeling fail to capture dynamic and non-linear dynamics of aquatic systems. Hence, efficacy of energy output is lost, and risk of operation failure is increased. Recent innovations in artificial intelligence such as machine learning and deep learning capabilities enhance power of modeling, forecasting, and optimization. Techniques such as long short-term memory (LSTM) networks, hybrid wavelet-convolutional neural networks (HW-CNNs), and physics-informed neural networks (PINNs) have significantly increased forecasting precision and versatility compared to conventional techniques. Artificial Intelligence-based models are seen to lower mean absolute percentage error (MAPE) in forecasting of tides and marine power by up to 35% and prediction-based maintenance frameworks lower unplanned downtime by more than 30%. Besides, usage of digital twins which are computerized replicas of physical assets, real-time assimilation of data have increased adaptive control ability by a significant percent, resulting in reduced structural fatigue and operating cost by 15 to 20%. Such contributions are not only technical but environmental and economical such as minimizing ecological disruptions and enhancing financial viability of projects. Overall, Artificial Intelligence-based prediction models are a disruptive methodology of scalable, efficient, and sustainable implementation of marine energy technology using tidal power.