基于CNN-BiGRU-NN模型的短期负荷预测方法

为充分挖掘蕴含在大量采集数据中的有效信息,提高短期负荷预测精度,提出一种基于卷积神经网络(CNN)和双向门控循环单元(BiGRU)、全连接神经网络(NN)的混合模型的短期负荷预测方法,将海量的历史负荷数据、气象信息、日期信息按时间滑动窗口构造特征图作为输入,先利用CNN提取特征图中的有效信息,构造特征向量,再将特征向量...

A Short-Term Load Forecasting Method Based on CNN-BiGRU-NN Model

In order to fully mine the effective information contained in a large number of collected data and improve the accuracy of short-term load forecasting, a short-term load forecasting method is proposed based on a hybrid model of convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and fully connected neural network (NN). The massive historical load data, meteorological information, and date information are taken to construct feature maps according to time sliding windows. Firstly, the CNN is used to extract valid information from the feature maps to construct feature vectors. And then, by taking the feature vectors as the inputs, the BiGRU-NN network is used to make short-term load forecasting. The load data in the test question A of the Ninth National Electrical Mathematics Modeling Contest held in 2016 are taken as an actual computation example, and the experimental results show that this method has higher accuracy in short-term load forecasting than GRU neural network, DNN neural network, and CNN-LSTM neural network.