A Hybrid Model of Particle Swarm Optimization for Wind Energy and Wind Power Through RNN

The increasing demand for sustainable energy sources has led to a great deal of study on optimizing wind energy systems. This study proposes a hybrid model that combines recurrent neural networks (RNN) and particle swarm optimization (PSO) to increase the efficiency of wind power forecasting and energy generation. Using previous data and meteorological conditions, the RNN is used to anticipate wind power output. The RNN's parameters are optimized via the PSO algorithm. The suggested model addresses the variable and irregular character of wind patterns in an effort to maximize energy output and enhance wind power forecasts through the integration of many methodologies. The hybrid model's performance is evaluated using actual wind farm data; the findings demonstrate a significant improvement in forecast accuracy and computing efficiency over traditional methods. This work demonstrates how advanced computational techniques and machine learning models can be used to enhance renewable energy systems and contribute to the development of safer and more effective wind energy solutions. Keywords - Particle Swarm Optimization, Recurrent Neural Networks, Wind Energy, Wind Power Forecasting, Renewable Energy