改进粒子群算法优化回声状态网络的电力需求预测研究

Echo state networks with improved particle swarm optimization algorithm for electricity demand forecasting

首先引入自适应算子对标准粒子群优化算法PSO的惯性权重和学习因子进行改进,以提高其探索当前空间和开发未知空间之间的平衡性。同时,采用非线性函数来构建回声状态网络ESN储备池内部状态之间的非线性关系。接着利用改进的粒子群优化算法APSO对非线性回声状态网络NESN的关键参数进行优化,以构建APSO-NESN组合预测模型。最后运用该模型进行电力需求预测。实验结果表明,相比自回归移动平均模型、多元线性回归、标准ESN及其他预测模型,APSO-NESN模型具有更高的预测精度。

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.