Wind turbine power prediction via deep neural network using hybrid approach

Due to the chaotic nature of wind speed, short-term wind power prediction is a challengeable one. A reliable and accurate wind power prediction model is necessary for the wind turbine industry. Based on their improved ability to cope with complicated nonlinear issues, an increasing number of deep learning-based models are being explored for wind power prediction as artificial intelligence technologies, especially in deep neural networks. In this research, the wind power prediction model is divided into three stages. The first stage is to collect data from wind turbines. The second stage is to apply the optimal tuning of the Deep Neural Network (DNN) to predict the wind power. Here, the hybrid algorithm termedBird Swarm Merged Seagull Optimizer(BSMSO) stipulates DNN's weight optimization points for wind power prediction; in addition, it reduces the time required for the same. Finally, the efficacy of the proposed BSMSO-DNN prediction model is proved by matching the statistical performance measures as regards toerror metrics with other existing techniques. The simulation results reveal thatthe proposed hybrid modelreduces the prediction errors significantly.