Abstract: Firstly, an adaptive operator is introduced to improve the inertial weights and learning factors of the standard particle swarm optimization algorithm (PSO) to improve the balance between the exploration of the current space and the unknown space. At the same time, a nonlinear function is used to construct the nonlinear relationship between the internal states of the Echo State Networks (ESN). Then the improved PSO (APSO) is used to optimize the key parameters of Nonlinear ESN (NESN) to propose an assembled prediction model named APSO-NESN. Finally, the model is used to solve electricity demand forecasting problems. The experimental results show that the APSO-NESN model has higher prediction accuracy than the ARIMA, MLR, standard ESN, and other models.

Key words: electricity consumption forecasting, echo state network (ESN), particle swarm optimization algorithm (PSO)