Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy

Energy, Год журнала: 2024, Номер 297, С. 131142 - 131142

Опубликована: Апрель 3, 2024

Язык: Английский

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Framework for multivariate carbon price forecasting: A novel hybrid model

Journal of Environmental Management, Год журнала: 2024, Номер 369, С. 122275 - 122275

Опубликована: Авг. 31, 2024

Язык: Английский

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Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration

Journal of Environmental Management, Год журнала: 2024, Номер 362, С. 121253 - 121253

Опубликована: Июнь 1, 2024

Язык: Английский

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An Optimized Extreme Learning Machine Composite Framework for Point, Probabilistic, and Quantile Regression Forecasting of Carbon Price

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106772 - 106772

Опубликована: Янв. 1, 2025

Язык: Английский

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MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting

Applied Energy, Год журнала: 2025, Номер 383, С. 125330 - 125330

Опубликована: Янв. 15, 2025

Язык: Английский

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Forecasting carbon price: A novel multi-factor spatial-temporal GNN framework integrating graph WaveNet and self-attention mechanism

Energy Economics, Год журнала: 2025, Номер unknown, С. 108318 - 108318

Опубликована: Фев. 1, 2025

Язык: Английский

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Carbon Price Point and Interval-Valued Prediction Based on a Novel Hybrid Model

Energies, Год журнала: 2025, Номер 18(5), С. 1054 - 1054

Опубликована: Фев. 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.

Язык: Английский

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Group dynamic game under bounded rationality in agreed transfer of China’s carbon trading secondary market

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110857 - 110857

Опубликована: Янв. 1, 2025

Язык: Английский

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Using explainable deep learning to improve decision quality: Evidence from carbon trading market

Omega, Год журнала: 2025, Номер unknown, С. 103281 - 103281

Опубликована: Янв. 1, 2025

Язык: Английский

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Forecasting carbon price in Hubei Province using a mixed neural model based on mutual information and Multi-head Self-Attention

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144960 - 144960

Опубликована: Фев. 1, 2025

Язык: Английский

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