Multi-time scale carbon emission of grid users forecasting based on hybrid neural network model

Shiya Ruan; Shuai Han; Leping Sun; Xiaoxuan Guo; Jianbin Lu

doi:10.1117/12.2684934

19 October 2023 Multi-time scale carbon emission of grid users forecasting based on hybrid neural network model

Shiya Ruan, Shuai Han, Leping Sun, Xiaoxuan Guo, Jianbin Lu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270924 (2023) https://doi.org/10.1117/12.2684934
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

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.

Citation Download Citation

Shiya Ruan, Shuai Han, Leping Sun, Xiaoxuan Guo, and Jianbin Lu "Multi-time scale carbon emission of grid users forecasting based on hybrid neural network model", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270924 (19 October 2023); https://doi.org/10.1117/12.2684934

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KEYWORDS

Carbon

Data modeling

Convolution

Eigenvectors

Neural networks

Statistical modeling

Power consumption

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