Floating photovoltaic systems provide better land use and higher energy output through water cooling effects

but accurate power forecasting remains challenging due to complex environmental factors and measurement errors. This study presents an improved teaching-learning-based optimization algorithm with extreme learning machine for floating photovoltaic power forecasting. The method uses an adaptive teaching factor that adjusts the balance between exploration and exploitation during optimization

replacing fixed teaching factors with continuous

iteration-based adjustment. The research evaluated the approach using comprehensive real data from a floating photovoltaic installation at Universiti Malaysia Pahang Al-Sultan Abdullah

Malaysia. The proposed method achieved superior forecasting accuracy compared to benchmark algorithms including standard teaching-learning-based optimization with extreme learning machine

manta rays foraging optimization with extreme learning machine

moth flame optimization with extreme learning machine

ant colony optimization with extreme learning machine and salp swarm algorithm with extreme learning machine. The improved teaching-learning-based optimization approach demonstrated a root mean squared error of 7.81 kW and coefficient of determination of 0.9386

outperforming all comparison methods with statistically significant improvements. The algorithm showed faster convergence

enhanced stability

and superior computational efficiency while maintaining accuracy suitable for real-time grid integration applications. Phase current measurements were identified as the most important predictors for floating photovoltaic power forecasting. The system achieved high prediction accuracy with most forecasts falling within acceptable error tolerance

making the proposed approach a reliable solution for floating photovoltaic power forecasting that supports grid integration and renewable energy deployment. The methodology addresses unique characteristics of aquatic solar installations while providing practical implementation viability for operational floating photovoltaic systems.