Assessing the effects of internet technology use on rural households' cooking energy consumption: Evidence from China

Energy, Год журнала: 2023, Номер 284, С. 128726 - 128726

Опубликована: Авг. 10, 2023

Язык: Английский

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Short-term modeling of carbon price based on fuel and energy determinants in EU ETS

Journal of Cleaner Production, Год журнала: 2023, Номер 417, С. 137970 - 137970

Опубликована: Июль 7, 2023

Язык: Английский

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A novel interval-valued carbon price analysis and forecasting system based on multi-objective ensemble strategy for carbon trading market

Expert Systems with Applications, Год журнала: 2023, Номер 244, С. 122912 - 122912

Опубликована: Дек. 14, 2023

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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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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Carbon market price prediction based on sequence decomposition-reconstruction-dimensionality reduction and improved deep learning model

Journal of Cleaner Production, Год журнала: 2023, Номер 425, С. 139063 - 139063

Опубликована: Сен. 29, 2023

Язык: Английский

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An ensemble self-learning framework combined with dynamic model selection and divide-conquer strategies for carbon emissions trading price forecasting

Chaos Solitons & Fractals, Год журнала: 2023, Номер 173, С. 113692 - 113692

Опубликована: Июнь 21, 2023

Язык: Английский

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Interval time series forecasting: A systematic literature review

Journal of Forecasting, Год журнала: 2023, Номер 43(2), С. 249 - 285

Опубликована: Сен. 20, 2023

Abstract Interval time series forecasting can be used for special symbolic data comprising lower and upper bounds plays an important role in handling the complexity, instability, uncertainty of observed objects. The purpose this research is to identify most widely definition interval series; classify existing into mature research, current focus, gaps within defined framework; recommend future directions research. To achieve goal, we have conducted a systematic literature review, search strategy planning, screening mechanism determination, document analysis, report generation. During planning stage, eight libraries are selected obtain extensive studies (total 525 targets). In screening‐mechanism determination through inclusion exclusion mechanism, that repetitive, low‐relevance, from other fields discarded, 125 finally selected. analysis tag‐based methods classification grids analyze shortlisted studies. results show there still numerous forecasting, such as establishment hybrid models, application multisource information, development evaluation techniques, expansion scenarios. report‐generation problems been solved encountered summarized, proposed. Finally, significant contribution provide overview easy reference by researchers facilitate further

Язык: Английский

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Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron

Physica A Statistical Mechanics and its Applications, Год журнала: 2024, Номер 636, С. 129543 - 129543

Опубликована: Янв. 22, 2024

Язык: Английский

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