The rapid growth in the number of electric vehicles has revealed an imbalance between the urban charging network layout and charging demand. This paper analyzes the influencing factors and principles of charging station planning. Taking full account of users’ travel rules, charging characteristics, charging pile utilization rate and other factors, a planning optimization model with the lowest sum of users’ time cost and charging station investment operation and maintenance cost is constructed, which promotes the scientific and rational distribution of charging facilities, improves the utilization rate of charging infrastructure, and provides a scientific basis for subsequent rational pile construction and operation.
KEYWORDS: Carbon, Data modeling, Convolution, Eigenvectors, Statistical modeling, Neural networks, Power grids, Power consumption, Education and training, Tunable filters
In order to fully understand the carbon emission of grid users and improve the accuracy of carbon emission forecasting results, this paper proposes a carbon emission forecasting method for grid users based on the convolutional neural network (CNN) and long-short term memory network (LSTM). The mapping relationship between energy consumption data and carbon emissions is explored by taking the historical energy consumption data of grid users as samples. Then the high-dimensional mapping relationship of carbon emission variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time series characteristic vector, which is input to the LSTM network. A carbon emission prediction model is established based on CNN-LSTM by training LSTM network parameters. Through the actual data verification, this research finds that compared with a single CNN, LSTM and ISPO-BP, the MAPE and RMSE of the CNN-LSTM model are reduced to 8.48% and 0.0526, while the R2 is improved to 97.33%, which indicates that the model constructed in this paper has a significant advantage in the accuracy and generalization ability of carbon emission prediction for grid users.
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