Skip to content
2000
Volume 18, Issue 10
  • ISSN: 2352-0965
  • E-ISSN: 2352-0973

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.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/raeeng/10.2174/0123520965316072240925075426
2025-01-06
2026-01-01

  • From This Site

    /content/journals/raeeng/10.2174/0123520965316072240925075426
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. CuiX.H. Re-estimation of carbon implicit in bilateral trade of manufacturing between China and the United States under the background of carbon peak.Ecol. Econ.201238072834
     [Google Scholar]
  2. ChenL.J. To achieve carbon peak and carbon neutralization, industrial parks must make contributions.Resource Regeneration202121520
     [Google Scholar]
  3. LiuQ A ZhangL Q JiY Q Evaluation of China's double-carbon energy policy based on the policy modeling consistency index.Utilities Policy202490101783
     [Google Scholar]
  4. Cui ZhangL W LiJ Review on comprehensive energy system evaluation and carbon emission accounting of industrial parks.J Shandong Jiandong Univ.20243901126134
     [Google Scholar]
  5. ChangY. Research on carbon emission prediction and peak path of urban industrial zone renewal.Shandong construction university2023
     [Google Scholar]
  6. JiangY. YinS. DongJ. KaynakO. A Review on soft sensors for monitoring, control and optimization of industrial processes.IEEE Sens. J.20212111128681288110.1109/JSEN.2020.3033153
     [Google Scholar]
  7. HuM.F. ZhengY.B. LiY.H. Research on peak carbon emission prediction of transportation in Hubei province under multi-scenario.J. Environ. Sci.20224464472
     [Google Scholar]
  8. HanN. LuoX.Y. Carbon emission peak prediction and emission reduction potential in Beijing-Tianjin-Hebei region under multi-scenario perspective.Ziran Ziyuan Xuebao20123751277128810.31497/zrzyxb.20220512
     [Google Scholar]
  9. WangL.B. ZhangY. Energy carbon emission factor decomposition and scenario prediction in China.Electric Power Construction2021420919
     [Google Scholar]
  10. ZhangJ.L. JiaF. Multi-model carbon peaking scenario prediction of China’s thermal power industry.Electric Power Construction201243051828
     [Google Scholar]
  11. ZhouS. LiW. 5 concentration -based on recurrent fuzzyneural network.2017 36 th Chinese Control Conference (CCC), Dalian, 2017, pp.3920-3924.
     [Google Scholar]
  12. AğbulutÜ. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms.Sustain. Prod. Consum.202229January14115710.1016/j.spc.2021.10.001
     [Google Scholar]
  13. LiH. YangX. ZhuH.L. Reducing carbon emissions in the architectural design process via transformer with cross-attention mechanism.Front. Ecol. Evol.202311124930810.3389/fevo.2023.1249308
     [Google Scholar]
  14. LimBryan ZohrenStefan Time-series forecasting with deep learning: A survey.Philos. Trans. R. Soc. A2021379219420200209
     [Google Scholar]
  15. HochreiterS. SchmidhuberJ. Long short-term memory.Neural Comput.1997981735178010.1162/neco.1997.9.8.17359377276
     [Google Scholar]
  16. BaiS.J. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.Preprint arXiv: 1803.012712018
     [Google Scholar]
  17. VaswaniA. ShazeerN. ParmarN. UszkoreitJ. JonesL. Attention is all you need. NeurIPS2017
     [Google Scholar]
  18. RangapuramSyama Sundar SeegerMatthias W GasthausJan StellaLorenzo WangYuyang JanuschowskiTim Deep state space models for time series forecasting.NeurIPS2018
     [Google Scholar]
  19. SalinasDavid FlunkertValentin GasthausJan JanuschowskiTim Deepar: Probabilistic forecasting with autoregressive recurrent networks.Int. J. Forecasting202036311811191
     [Google Scholar]
  20. ChildR. GrayS. RadfordA. Generating long sequences with sparse transformers.Preprint arXiv:1904.105092019
     [Google Scholar]
  21. YangZ. YangD. DyerC. Hierarchical attention networks for document classification.Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologiesSan Diego, California, Jun 2016, pp. 1480-1489.2016
     [Google Scholar]
  22. ZandiehA. HanI. DaliriM. KarbasiA. Kdeformer: Accelerating transformers via kernel density estimation.International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. PMLR 202, 2023.
     [Google Scholar]
  23. HanI. JarayamR. KarbasiA. Hyperattention: Long-context attention in near-linear time.Preprint arXiv: 2310.05869,2023
     [Google Scholar]
  24. LiuM. ZengA. ChenM. Scinet: Time series modeling and forecasting with sample convolution and interaction.Adv. Neural Inf. Process. Syst.20223558165828
     [Google Scholar]
  25. LiuM. ZengA. XuZ. Time series is a special sequence: Forecasting with sample convolution and interaction.Preprint arXiv: 2106.093052021
     [Google Scholar]
  26. WangY.Y. YangZ.J. Typical scenarios and paths of carbon metering for low-carbon development.Oil & Gas Storage & Transportation202342012431
     [Google Scholar]
  27. HeK.M. ZhangX.Y. RenS.Q. SunJ. Deep residual learning for image recognition.Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2016, pp. 770-778.
     [Google Scholar]
  28. KeK. HongbinS. ChengkangZ. BrownC. Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network.Evol. Intell.201912338539410.1007/s12065‑018‑00196‑0
     [Google Scholar]
  29. KongW DongZ Y JiaY Short-term residential load forecasting based on LSTM recurrent neural network.IEEE Trans. Smart Grid2017101841851
     [Google Scholar]
  30. ZangHai-xiang XuRuiqi LiuJing-Xuan Multi-task user short-term load forecasting based on multi-dimensional fusion feature and Convolutional neural network.Autom. Power Syst.2023112
     [Google Scholar]
  31. HuiS.U.N. FanY. ZhengnanG.A.O. MI-BILSTM short-term load forecasting considering feature importance fluctuation.Dianli Xitong Zidonghua2022460895103
     [Google Scholar]
  32. YaoChengwen PingYang LiuZejian Load forecasting method based on CNN-GRU hybrid neural network.Power Grid Technol.202044934163424
     [Google Scholar]
  33. ZengA. ChenM. ZhangL. XuQ. Are transformers effective for time series forecasting.Proc. Conf. AAAI Artif. Intell.2023379111211112810.1609/aaai.v37i9.26317
     [Google Scholar]
/content/journals/raeeng/10.2174/0123520965316072240925075426
Loading
HA-SCINet based Carbon Emission Prediction of the Typical Park
Recent Advances in Electrical & Electronic Engineering 18, 1891 (2025); https://doi.org/10.2174/0123520965316072240925075426
/content/journals/raeeng/10.2174/0123520965316072240925075426
/content/journals/raeeng/10.2174/0123520965316072240925075426
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Affluence and Technology (STIRPAT); Carbon emission prediction; HA-SCINet; mean absolute error (MAE); mean square error (MSE); SCInet; Stochastic Impacts by Regression Population

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test