An extreme learning machine model optimized based on improved golden eagle algorithm for wind power forecasting

Wind energy has been widely valued by countries worldwide as clean energy. However, wind power is often affected by the weather. An accurately predicting wind power to ensure power supply has become an urgent problem to be solved. Therefore, this paper proposes an extreme learning machine model based on optimizing the improved Golden Eagle algorithm. First, the Golden Eagle algorithm is improved, and the convergence speed of the algorithm is enhanced through a parallel strategy. And achieved excellent results in 12 CEC2017 benchmark functions; then, we combined the improved Golden Eagle algorithm with the extreme learning machine to optimize the input weights and biases of the extreme learning machine; finally, we will propose the POGEO-ELM model is used for wind power forecasting of NWP data, and the experimental results show that the model proposed in this paper has good forecasting ability.