Application of Machine Learning Model for Forecasting of Floating PV Cell

floating solar photovoltaic (FPV) systems are gaining global attention due to their efficient use of water surfaces and natural cooling effects. However, the variability in environmental factors affecting FPV systems presents a significant challenge for accurate power forecasting. This paper investigates short-term forecasting of FPV power output using machine learning (ML) techniques. Models including Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM) networks are developed and compared. A detailed analysis of feature importance, model performance, and error metrics is presented. The results demonstrate that LSTM achieves superior accuracy with a Mean Absolute Percentage Error (MAPE) of 2.5%, making it a promising tool for FPV power management. Key word - Floating solar photovoltaic (FPV), short-term forecasting, machine learning, Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Random Forest (RF), renewable energy, solar energy forecasting, time-series analysis, power output prediction.