Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction

Wind energy is a green source of electricity that is growing faster than other renewable energies. However, dependent mainly on wind speed, this source is characterized by the randomness and fluctuation that makes challenging optimal management. In order to remedy this inconvenience, it is essential to predict meteorological data or power produced by generators. In this paper, we present a wind power forecasting approach based on regularized extreme learning machine algorithm (R-ELM), particle swarm optimization method (PSO), and AutoEncoder network (AE) so-called AutoEncoder-optimal regularized extreme learning machine (AE-ORELM). Firstly, we train the AE model by the ELM algorithm. Then, the output weights resulting are used as the input weights of the R-ELM model. Furthermore, the PSO method is used to optimally select hyperparameters of the whole model, namely the regularization parameter and the number of hidden nodes in the hidden layer. The simulation results show that the proposed AE-ORELM can achieve better testing accuracy with a faster training time compared to related models.