Improving Carbon Emission Prediction with Multiscale Decomposition and Transformer Networks
DOI:
https://doi.org/10.13052/spee1048-5236.4435Keywords:
Variational mode decomposition, transformer, carbon neutrality, time series analysis, carbon emission forecastingAbstract
With the intensification of global climate change, carbon emissions have become a critical challenge in achieving sustainable development. Accurate carbon emission forecasting plays a key role in formulating carbon neutrality policies and optimizing energy structures. However, current mainstream forecasting methods often face limitations in handling complex nonlinear time series data, including insufficient modeling capabilities, susceptibility to mode mixing, and low prediction accuracy. To address these issues, this paper proposes a carbon emission forecasting model that combines Variational Mode Decomposition (VMD) with an optimized Transformer-ResLSTM structure. Specifically, VMD is first employed to decompose the original time series into a set of Intrinsic Mode Functions (IMFs) with distinct frequency characteristics, effectively mitigating mode mixing issues. Then, a specially designed Transformer-ResLSTM network is used to model each IMF individually, extracting critical information from both global and local features. Finally, the prediction values are generated through a reconstruction and error correction module, improving both prediction accuracy and robustness. Experiments conducted on the CARMA and GEFCom datasets demonstrate that the proposed model outperforms multiple baseline models across all evaluation metrics, such as RMSE, MAE, MAPE, and R2, highlighting its superiority and practicality in carbon emission forecasting tasks. This model provides an effective solution to the challenges of nonlinear modeling in carbon emission forecasting and offers robust technical support for achieving carbon neutrality goals.
Downloads
References
H. Shi, A. Wei, X. Xu, Y. Zhu, H. Hu, and S. Tang, “A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen’s carbon market in China,” Journal of Environmental Management, vol. 352, pp. 120131, 2024.
X. Huang, S. Zhu, and Y. Ren, “A Semantic Matching Method of E-Government Information Resources Knowledge Fusion Service Driven by User Decisions,” Journal of Organizational & End User Computing, vol. 35, no. 1, 2023.
F. Zhou, Z. Huang, and C. Zhang, “Carbon price forecasting based on CEEMDAN and LSTM,” Applied energy, vol. 311, pp. 118601, 2022.
H. Ran, W. Li, L. Li, S. Tian, X. Ning, and P. Tiwari, “Learning optimal inter-class margin adaptively for few-shot class-incremental learning via neural collapse-based meta-learning,” Information Processing & Management, vol. 61, no. 3, pp. 103664, 2024.
Z. Han, B. Cui, L. Xu, J. Wang, and Z. Guo, “Coupling LSTM and CNN neural networks for accurate carbon emission prediction in 30 Chinese provinces,” Sustainability, vol. 15, no. 18, pp. 13934, 2023.
X. Lin, X. Zhu, M. Feng, Y. Han, and Z. Geng, “Economy and carbon emissions optimization of different countries or areas in the world using an improved Attention mechanism based long short term memory neural network,” Science of the Total Environment, vol. 792, pp. 148444, 2021.
L. Zhang, J. Liu, Y. Wei, D. An, and X. Ning, “Self-supervised learning-based multi-source spectral fusion for fruit quality evaluation: A case study in mango fruit ripeness prediction,” Information Fusion, vol. 117, pp. 102814, 2025.
J. Huang, X. Yu, D. An, X. Ning, J. Liu, and P. Tiwari, “Uniformity and deformation: A benchmark for multi-fish real-time tracking in the farming,” Expert Systems with Applications, vol. 264, pp. 125653, 2025.
J. Cai, I. Sharkawi, and S. I. Taasim, “The role of deep learning in supply chain collaboration and cooperation,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–23, 2024.
Y. Huang, L. Shen, and H. Liu, “Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China,” Journal of Cleaner Production, vol. 209, pp. 415–423, 2019.
J. Chen, L. Zheng, W. Che, L. Liu, H. Huang, J. Liu, X. Chang, and P. Qiu, “A Method for Measuring Carbon Emissions from Power Plants Using a CNN-LSTM-Attention Model with Bayesian Optimization,” Case Studies in Thermal Engineering, pp. 105334, 2024.
M. Li, and W. Xiao, “Research on the Effect of E-Leadership on Employee Innovation Behavior in the Context of “Self” and “Relationship”,” Journal of Organizational & End User Computing, vol. 35, no. 1, 2023.
L. Fan, C. Wang, and Z. Lu, “Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–24, 2024.
X. Zhao, F. Zou, F. Guo, and S. Jin, “Real-Time Carbon Emission Monitoring and Prediction Method of Expressway Based on LSTM.” pp. 68–78.
Y. Zhou, Z. Wang, S. Zheng, L. Zhou, L. Dai, H. Luo, Z. Zhang, and M. Sui, “Optimization of automated garbage recognition model based on resnet-50 and weakly supervised cnn for sustainable urban development,” Alexandria Engineering Journal, vol. 108, pp. 415–427, 2024.
T. Li, and J. Song, “Deep Learning-Powered Financial Product Recommendation System in Banks: Integration of Transformer and Transfer Learning,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–29, 2024.
C. Saleh, N. R. Dzakiyullah, and J. B. Nugroho, “Carbon dioxide emission prediction using support vector machine.” p. 012148.
X. Xing, B. Wang, X. Ning, G. Wang, and P. Tiwari, “Short-term OD flow prediction for urban rail transit control: A multi-graph spatiotemporal fusion approach,” Information Fusion, pp. 102950, 2025.
Y. Fang, X. Lu, and H. Li, “A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage,” Journal of Cleaner Production, vol. 328, pp. 129657, 2021.
X. Cui, S. E, D. Niu, B. Chen, and J. Feng, “Forecasting of carbon emission in China based on gradient boosting decision tree optimized by modified whale optimization algorithm,” Sustainability, vol. 13, no. 21, pp. 12302, 2021.
Q. Liu, H. Tang, L. Wu, and Z. Chao, “Deep Learning-Driven E-Commerce Marketing Communication for Recommending Shopping System and Optimizing User Experience,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–28, 2024.
H. Cao, L. Han, M. Liu, and L. Li, “Spatial differentiation of carbon emissions from energy consumption based on machine learning algorithm: A case study during 2015–2020 in Shaanxi, China,” Journal of Environmental Sciences, vol. 149, pp. 358–373, 2025.
W. Sun, and C. Huang, “Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency,” Journal of Cleaner Production, vol. 338, pp. 130414, 2022.
P. Zhang, X. Yu, X. Bai, J. Zheng, X. Ning, and E. R. Hancock, “Fully Decoupled End-to-End Person Search: An Approach without Conflicting Objectives,” International Journal of Computer Vision, pp. 1–22, 2025.
L. Wang, G. Liu, and H. Hamam, “Enhancing Logistics Optimization: A Double-Layer Site-Selection Model Approach,” Journal of Organizational and End User Computing (JOEUC), vol. 36, no. 1, pp. 1–15, 2024.
X. Liu, J. Zhang, Y. Hu, J. Liu, S. Ding, G. Zhao, Y. Zhang, J. Li, and Z. Nie, “Carbon emission evaluation method and comparison study of transformer substations using different data sources,” Buildings, vol. 13, no. 4, pp. 1106, 2023.
X. Zhang, and D. Wang, “A GNN-based Day Ahead Carbon Intensity Forecasting Model for Cross-Border Power Grids.” pp. 361–373.
J. Liao, J. Hu, P. Chen, L. Zhu, Y. Wu, Z. Cai, H. Wu, and M. Wang, “Prediction of the transient emission characteristics from diesel engine using temporal convolutional networks,” Engineering Applications of Artificial Intelligence, vol. 127, pp. 107227, 2024.
T. Wen, Y. Liu, Y. he Bai, and H. Liu, “Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model,” Heliyon, vol. 9, no. 11, 2023.
C. DeMatteo, J. Jakubowski, K. Stazyk, S. Randall, S. Perrotta, and R. Zhang, “The Headaches of Developing a Concussion App for Youth: Balancing Clinical Goals and Technology,” International Journal of E-Health and Medical Communications (IJEHMC), vol. 15, no. 1, pp. 1–20, 2024.
C. Maino, D. Misul, A. Di Mauro, and E. Spessa, “A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions,” Energy and AI, vol. 5, pp. 100073, 2021.
W. I. Almayyan, and B. A. AlGhannam, “Detection of Kidney Diseases: Importance of Feature Selection and Classifiers,” International Journal of E-Health and Medical Communications (IJEHMC), vol. 15, no. 1, pp. 1–21, 2024.
A. Vaswani, “Attention is all you need,” Advances in Neural Information Processing Systems, 2017.
C. Fan, H. Xie, J. Tao, Y. Li, G. Pei, T. Li, and Z. Lv, “ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition,” Biomedical Signal Processing and Control, vol. 87, pp. 105422, 2024.
L. Han, R. Zhang, X. Wang, A. Bao, and H. Jing, “Multi-step wind power forecast based on VMD-LSTM,” IET renewable power generation, vol. 13, no. 10, pp. 1690–1700, 2019.
S. Afsah, and E. Ness, “Carbon monitoring for action (CARMA): Climate campaign built on questionable data-A due diligence report on CARMA’s data and methodology,” Available at SSRN 1133432, 2008.
R. Nedellec, J. Cugliari, and Y. Goude, “GEFCom2012: Electric load forecasting and backcasting with semi-parametric models,” International Journal of forecasting, vol. 30, no. 2, pp. 375–381, 2014.
Q. Wan, Z. Zhang, L. Jiang, Z. Wang, and Y. Zhou, “Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model,” arXiv preprint arXiv:2406.13987, 2024.
H. Chen, H. Wu, T. Kan, J. Zhang, and H. Li, “Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction,” International Journal of Electrical Power & Energy Systems, vol. 154, pp. 109420, 2023.
C. E. Onu, J. T. Nwabanne, P. E. Ohale, and C. O. Asadu, “Comparative analysis of RSM, ANN and ANFIS and the mechanistic modeling in eriochrome black-T dye adsorption using modified clay,” South African Journal of Chemical Engineering, vol. 36, pp. 24–42, 2021.
Y. Wang, M. Huang, X. Zhu, and L. Zhao, “Attention-based LSTM for aspect-level sentiment classification.” pp. 606–615.
J. You, X. Huang, Y. Li, Q. Yu, and W. Ampomah, “Enhancing Production Forecasting in Fractured Shale Gas Reservoirs Using a Hybrid ARIMA-EMD-LSTM Approach.” p. D021S043R005.
https://aeeeuropeenergy.com/
