| Based on the data from State Grid Customer Service Center,this thesis conducts a research on traffic forecasting models,which mainly includes the following aspects:1.The theoretical basis of traffic is studied,including the definition of traffic,the principle of traffic forecasting,the classification of traffic forecasting,the steps of traffic forecasting and the error analysis method of traffic forecasting results.2.The classification of electrical traffic is analyzed,and the main key factors affecting the traffic,namely,the number of customers who are in power failure due to electricity arrears,the number of fault outages,time and temperature are confirmed.3.A Long-short Term Memory(LSTM)model is built,and innovatively Convolutional Neural Networks(CNN)is combined to construct a Convolutional Long-Short Term Memory(CLSTM)model.Then the self-attention mechanism is considered,and finally the Convolutional Long-short Term Memory Based On Self-Attention(ACLSTM)model is formed.Selecting Hubei and Jiangsu with significant business differences as typical provinces to analyze,compare the prediction results of the ACLSTM model with the CLSTM and LSTM models,it is confirmed that the ACLSTM model can predict traffic with high accuracy,and the prediction performance is significantly better than CLSTM And LSTM model.4.Combining the traffic forecast ACLSTM model with the Erlang-A formula,a call center personnel regulation model is established,and it proves that the model plays an important role in the operation and management of the call center.5.Prospects for future research directions mainly include the verification of the ACLSTM model’s prediction effect during special events such as typhoons.In summary,this research is another innovative application of deep learning in the field of traffic forecasting.At the same time,the model has strong generalization and provides an important reference for the application of deep learning in various fields. |
PDF Full Text Request