Carbon price prediction based on multidimensional association rules and optimized multi-factor LSTM model DOI
Xinqi Tu, Lianlian Fu, Lingling Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136768 - 136768

Published: May 1, 2025

Language: Английский

Carbon Price Point and Interval-Valued Prediction Based on a Novel Hybrid Model DOI Creative Commons
Haoyu Chen,

Qunli Wu,

C. Han

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1054 - 1054

Published: Feb. 21, 2025

Accurate carbon price forecasting enables the steady operation of trading market and optimal resource allocation while also empowering participants to understand dynamics make informed decisions, ultimately supporting sustainable development in market. While early research primarily focused on point single-value price, recent studies have shifted towards interval prediction, although there is still a lack dedicated developing models for interval-valued predictions. The importance lies its ability better capture upper lower bounds range across different time dimensions, thereby revealing intrinsic patterns trends fluctuations assisting comprehensively volatility. This study offers novel approach based CEEMDAN-CNN-BiLSTM-SENet hybrid model, providing framework both model makes more comprehensive analysis possible by combining predictions from these two approaches. In case using Hubei market’s data, mean absolute percentage error pricing was 0.8125%, with MAPE highest lowest prices being 1.8898% 1.7852%, respectively—both outperforming other comparative models. results demonstrate that this can measure effectively.

Language: Английский

Citations

1
Assessment of urban flood susceptibility based on a novel integrated machine learning method DOI
Haidong Yang, Ting Zou, Biyu Liu

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 5, 2024

Language: Английский

Citations

3
Group dynamic game under bounded rationality in agreed transfer of China’s carbon trading secondary market DOI
Zhen Peng, Zitao Hong

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 110857 - 110857

Published: Jan. 1, 2025

Language: Английский

Citations

0
Carbon price prediction based on multidimensional association rules and optimized multi-factor LSTM model DOI
Xinqi Tu, Lianlian Fu, Lingling Wang

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 136768 - 136768

Published: May 1, 2025

Language: Английский

Citations

0