基于Dropout-ILSTM网络的短期电力负荷预测

针对传统BP神经网络难以处理电力负荷数据间关联的问题,提出了一种基于Dropout的改进的长短期记忆神经网络结构用于短期电力负荷预测。这种改进的长短期记忆神经网络(Improved LSTM,ILSTM),通过将长短期记忆网络的多个时间步输入与输出矢量进行全连接,增强了对目标系统中线性成分的表征;使用Dropout对ILSTM网络进行优化,提高了网络的泛化能力,同时减少了模型的训练时间;以日期、温度、电价和电力负荷数据作为输入构建了Dropout-ILSTM电力负荷预测模型。以AEMO提供的新南威尔士州电力负荷数据作为测试用例,实验结果表明,相较其它神经网络模型,文中所提出的Dropout-ILSTM模型预测精度更高、泛化能力更强,适用于不同预测宽度的电力负荷预测。

Short-term Power Load Forecasting Based on Dropout-ILSTM Network

Considering that it is difficult for traditional BP neural network to deal with the correlation between power load data,this paper proposes an Improved Long Short-Term Memory based on Dropout for short-term power load forecasting.Firstly,an Improved Long Short-Term Memory(ILSTM)network is proposed,which enhances the representation of linear components in the target system by fully connecting multiple time-step inputs of long-and short-term memory networks with output vectors.Secondly,using Dropout Optimizing the ILSTM network improves the generalization ability of the network and reduces the training time of the model.Finally,the Dropout-ILSTM power load forecasting model is constructed with the date,temperature,electricity price and power load data as inputs.This paper uses the NSW electric load data provided by AEMO as a test case.The experimental results show that the proposed Dropout-ILSTM model has higher prediction accuracy and more generalization ability than other neural network models.And the Dropout-ILSTM model is suitable for power load forecasting with different prediction widths.