Short-term power load forecasting based on IWOA-GRU

Abstract

This paper proposes a short-term load prediction model based on the improved whale optimization algorithm (IW0A) optimized gated recurrent neural network(GRU) to address the issue of strong unpredictability of electric load and low forecast accuracy. First, the whale population is initialized by S chaotic mapping to enhance the population diversity and improve the quality of the initial solution; second, a nonlinear convergence factor is proposed to balance the global and local search ability of the algorithm and improve the convergence speed in order to avoid the defects that the standard whale optimization algorithm is easy to fall into local optimum and slow convergence speed when solving the GRU parameter optimization problem. Finally, WOA is used to automatically determine the best parameters and create the IWOA-GRU load prediction model by optimizing the number of layer neurons, learning rate, and other factors. The results show that when compared to the prediction methods used by LSTM, GRU, PSO-GRU, RSO-GRU, and WOA-GRU, the proposed model may successfully increase convergence speed and prediction accuracy.