Title:HA-SCINet based Carbon Emission Prediction of the Typical Park
Volume: 18
Author(s): Wang Zeli, Liu Zeyu, Song Xupeng, Wei Guangtao, Li Mingchun, Huang Hongjun, Shao Weiqi and Li Yuancheng*
Affiliation:
- School of Control and Computer Engineering, North China Electric Power University, Beijing
102206, China
Keywords:
Carbon emission prediction, HA-SCINet, SCInet, mean square error (MSE), mean absolute error (MAE), Stochastic Impacts by Regression Population, Affluence and Technology (STIRPAT).
Abstract:
Background: With the rapid economic growth and the accelerated process of industrialization,
the production activities of enterprises within parks have significantly increased, leading
to a continuous rise in carbon emissions. Under the context of the "dual carbon" goals, studying
the prediction of carbon emissions in typical parks holds significant practical importance. It is not
only a key measure to address climate change but also an important pathway to achieve sustainable
development.
Objective: In order to predict the carbon emissions of the typical park more accurately, we propose
a carbon emissions prediction model HA-SCINet.
Methods: The model uses a recursive downsampling-convolution-interaction architecture. In each
layer, the long-term dependence in time series data is extracted by HyperAttention. Then through
the L-layer SCI-Block of the binary tree structure, the down-sampling interactive learning extracts
both short-term and long-term dependencies. These extracted features are merged and reorganized,
and added to the original time series to generate a new sequence with enhanced predictability.
Finally, employ a fully connected network for forecasting the enhanced sequence. The carbon
emission data of the typical park serve as input, leading to higher accuracy prediction results
through the Stacked K-layer stacked HA-SCINet.
Results: The mean square error (MSE) and mean absolute error (MAE) of HA-SCINet prediction
model are 0.0819 and 0.204 respectively, outperforming the mainstream Dlinear and Nlinear models.
Conclusion: The experimental results show that the devised model outperforms in predicting carbon
emissions, and is better suited for forecasting carbon emissions within the context of the typical
park.
