基于多尺度跳跃深度长短期记忆网络的短期多变量负荷预测

近年来，以循环神经网络（RNN）为主体构建的预测模型在短期电力负荷预测中取得了优越的性能。然而，由于RNN不能有效捕捉存在于短期电力负荷数据的多尺度时序特征，因而难以进一步提升负荷预测精度。为了捕获短期电力负荷数据中的多尺度时序特征，提出了一种基于多尺度跳跃深度长短期记忆（MSD-LSTM）网络的短期电力负荷预测模型。具体来说，以长短期记忆（LSTM）网络为主体构建预测模型能够较好地捕获长短期时序依赖，从而缓解时序过长时重要信息容易丢失的问题。进一步地，采用多层LSTM架构并且对各层设置不同的跳跃连接数，使得MSD-LSTM的每一层能够捕获不同时间尺度的特征。最后，引入全连接层把各层提取到的多尺度时序特征进行融合，再利用该融合特征进行短期电力负荷预测。实验结果表明，与单层LSTM和多层LSTM相比，MSD-LSTM的均方误差总体下降了10%。可见MSD-LSTM能够更好地提取短期负荷数据中的多尺度时序特征，从而提高短期电力负荷预测的精度。

Multi-scale skip deep long short-term memory network for short-term multivariate load forecasting

In recent years,the short-term power load prediction model built with Recurrent Neural Network(RNN)as main part has achieved excellent performance in short-term power load forecasting.However,RNN cannot effectively capture the multi-scale temporal features in short-term power load data,making it difficult to further improve the load forecasting accuracy.To capture the multi-scale temporal features in short-term power load data,a short-term power load prediction model based on Multi-scale Skip Deep Long Short-Term Memory(MSD-LSTM)was proposed.Specifically,a forecasting model was built with LSTM(Long Short-Term Memory)as main part,which was able to better capture long shortterm temporal dependencies,thereby alleviating the problem that important information is easily lost when encountering the long time series.Furthermore,a multi-layer LSTM architecture was adopted and different skip connection numbers were set for the layers,enabling different layers of MSD-LSTM can capture the features with different time scales.Finally,a fully connected layer was introduced to fuse the multi-scale temporal features extracted by different layers,and the obtained fusion feature was used to perform the short-term power load prediction.Experimental results show that compared with LSTM,MSDLSTM achieves lower Mean Square Error(MSE)with the reduction of 10%in general.It can be seen that MSD-LSTM can better capture multi-scale temporal features in short-term power load data,thereby improving the accuracy of short-term power load forecasting.