ANN and LSTM Models for Hourly Power Prediction of a Tropical Floating Photovoltaic System

The work focuses on the application of advanced computational techniques to floating photovoltaic (FPV) systems, specifically targeting performance prediction and day-ahead power forecasting.

The significant research contribution lies in addressing the dual complexities of FPV systems—namely, their unique environmental operating conditions (such as the cooling effects of water bodies) and the inherent intermittency of solar energy. By leveraging and comparing cutting-edge machine learning and deep learning architectures, such as Deep LSTM-RNNs and Artificial Neural Networks (ANN), this research advances the state-of-the-art in predictive modeling. This contribution provides the solar energy sector with highly accurate, day-ahead forecasting frameworks that are critical for optimizing grid integration, improving operational efficiency, and validating the thermodynamic advantages of floating solar arrays over traditional land-based systems.