Research on Simulation of Short-term Power Load Forecasting based on Neural Network
Chen Ya1, Li Ping2
1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021; 2. Ningxia Key Laboratory of Intelligent Sensing for Desert Information, Yinchuan 750021
Abstract：Short-term load forecasting models based on BP neural network and Elman neural network are established in order to improve the accuracy of short-term power load forecasting.In order to improve the convergence rate, the BP neural network is optimized by the additional momentum method. For Elman neural network is easy to fall into the local extremum, so improve the incentive function and use the LM algorithm to optimize the learning algorithm.Matlab simulation results show that the improved Elman neural network model is better than the BP neural network model with high accuracy and fast convergence speed, which is more suitable for dynamic problems.
陈亚, 李萍. 基于神经网络的短期电力负荷预测仿真研究[J]. 电气技术, 2017, 18(1): 26-29.
Chen Ya, Li Ping. Research on Simulation of Short-term Power Load Forecasting based on Neural Network. Electrical Engineering, 2017, 18(1): 26-29.
2017, Vol. 18 
