基于SFO-GRU模型的短期负荷预测

Short Term Load Forecasting Based on SFO-GRU Model

为提高短期负荷预测精度,提出一种基于旗鱼优化算法(SFO)和GRU神经网络的短期负荷预测模型。通过使用负荷、气象等真实数据对GRU神经网络的权重参数进行训练并建立GRU预测模型,同时采用旗鱼优化算法(SFO)优化GRU模型的迭代次数、学习率和隐藏层神经元数量,仿真实验结果表明,与BP、LSTM等经典预测模型相比,SFO-GRU模型具有更高的预测精度。

In order to improve the accuracy of short term load forecasting,a short term load forecasting model based on sailed fish optimizer(SFO)and GRU neural network is proposed.The weight parameters of GRU neural network are trained with actual data such as load and weather,and the GRU prediction model is established.At the same time,the sailed fish optimizer(SFO)is used to optimize the number of iterations,learning rate,and number of hidden layer neurons.The simulation experiment result shows that SFO-GRU model has higher prediction accuracy than BP,LSTM and other classic forecasting models.