基于改进GRU的短期电力负荷预测方法

Short-Term Power Load Forecasting Method Based on Improved GRU

为提高电力负荷预测的准确性,采用灰狼优化(Grey Wolf Optimizer,GWO)算法对门控循环单元(Gated Recurrent Unit Neural Network,GRU)神经网络进行优化,并进行短期电力负荷预测。首先预处理数据并量化影响因素,然后搭建基于GWO超参数优化的GRU神经网络模型,最后与其他模型对比得出预测结果。实验结果显示,该方法拟合度高,收敛速度快,有较好的预测效果。

In order to improve the accuracy of power load forecasting,this paper uses the Grey Wolf Optimizer(GWO)algorithm to optimize the Gated Recurrent Unit Neural Network(GRU)neural network and perform short-term power load forecasting.First,preprocess the data and quantify the influencing factors,then build a GRU neural network model based on GWO optimized hyperparameters,and finally compare with other models to get the prediction results.The experimental results show that the method has a high degree of fit,a fast convergence rate,and a good prediction effect.