PREDICTION OF POWER GENERATION IN WIND TURBINES

Wind energy is a promising alternative source of clean and renewable energy that can help mitigate climate change. Accurate prediction of power generation in wind turbines is critical for the effective integration of wind energy into the power grid. In this study, we compare the performance of two popular machine learning algorithms, linear regression and random forest, for power generation prediction in wind turbines. We use wind speed and direction as input variables and power generation as the output variable. Our results show that random forest outperforms linear regression in terms of prediction accuracy and robustness.