THE ROLE OF ARTIFICIAL INTELLIGENCE IN PREDICTING AND OPTIMIZING TIDAL POWER OUTPUT

Abstract

The increasing global demand for clean and sustainable energy has encouraged the development of advanced renewable energy technologies. Tidal power is considered a promising renewable energy source due to its predictability and high energy potential; however, variations in tidal conditions, environmental factors, and operational challenges can affect power generation efficiency. Artificial Intelligence (AI) has emerged as an effective approach for improving tidal power forecasting, optimizing energy production, and enhancing the reliability of tidal energy systems. This research explores the role of AI techniques in predicting and optimizing tidal power output by analyzing the applications of machine learning, deep learning, and intelligent optimization algorithms. AI-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, and deep learning approaches are evaluated for their ability to process complex tidal data, identify patterns, and provide accurate power generation forecasts. AI-driven optimization strategies are investigated for improving turbine performance, reducing operational losses, and maximizing energy output under changing tidal conditions. The study highlights that AI integration can significantly enhance prediction accuracy, support real-time decision-making, and contribute to the efficient management of tidal power systems. Challenges related to data availability, computational requirements, model complexity, and environmental uncertainties remain important considerations. The research concludes that AI-based solutions have significant potential to accelerate the adoption of tidal energy and improve the sustainability and efficiency of future renewable energy systems.