并行多模型融合的混合神经网络超短期负荷预测

针对输入数据特征多时负荷预测模型精度提升难的问题,文章提出一种并行多模型融合的混合神经网络超短期负荷预测方法。将卷积神经网络(convolutional neural network,CNN)与门控循环单元神经网络(gated recurrent unit neural network,GRU-NN)并行,分别提取局部特征与时序特征,将2个网络结构的输出拼接并输入深度神经网络(deep neural network,DNN),由DNN进行超短期负荷预测。最后应用负荷与温度数据进行预测实验,结果表明相比于GRUNN网络结构、长短期记忆(long short term memory,LSTM)网络结构、串行CNN-LSTM网络结构与串行CNN-GRU网络结构,所提方法具有更好的预测性能。

Ultra-short-term Load Forecasting Using Hybrid Neural Network Based on Parallel Multi-model Combination

For the purpose of addressing the difficulty of improving load forecasting accuracy brought by enormous input data features, a method based on hybrid neural network using parallel multi-model combination is proposed. In order to respectively extract local features and time-series features, this paper places the convolutional neural network (CNN) in parallel with the gated recurrent unit (GRU) structure, then concatenates the output of two network structures and inputs to a deep neural network, uses deep neural network to perform load forecasting. Through a prediction experiment of load and temperature data by using the proposed method, the experiment results show that, compared with GRU-NN model, long short term memory (LSTM) model, serial CNN-LSTM network model and serial CNN-GRU network model, the proposed method shows better prediction performance.