Electric power forecasting based on bee colony optimized extreme learning machine algorithm

Aiming at the problems of strong randomness of electric power load sequence and insufficient load forecasting accuracy, an electric power forecasting model based on artificial bee colony algorithm（ABC） optimized extreme learning machine（ELM） is proposed. The empirical mode decomposition method is used to stabilize the original power load sequence, and the eigenmode function components and residual components containing local characteristic signals of different time scales are obtained; the artificial bee colony algorithm is used to optimize the extreme learning machine model to learn each mode. The time sequence law of the components and the component prediction are performed, and the predicted values of the modal components are fused and superimposed to obtain the final prediction result. The experimental results show that the proposed prediction model can accurately predict the power consumption, and has better generalization performance and higher prediction accuracy.