基于神经网络的短期电力负荷预测仿真研究

Research on Simulation of Short-term Power Load Forecasting based on Neural Network

为了提高短期电力负荷预测精度,分别建立了基于BP神经网络和Elman神经网络的短期负荷预测模型。采用附加动量法优化BP神经网络以提高其收敛速度;针对Elman神经网络易陷入局部极值的缺点,改进其激励函数并采用LM算法优化学习算法。Matlab仿真结果表明,改进后的Elman神经网络模型比BP神经网络模型的预测精度高,收敛速度快,更适合处理动态问题。

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.