Prediction of daily electricity carbon emission factors for high energy-consuming enterprises based on temporal feature optimization and hybrid intelligent algorithms Article Swipe
Kefei Guan
,
Dexin Tan
,
Qiuzi Zhong
,
Pei Li
,
J. Zhong
,
Wei Cao
,
Hongyan Zhang
,
Chong Liang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1063/5.0287652
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1063/5.0287652
With the advancement of China’s “Dual Carbon” strategic goals, carbon emission monitoring and forecasting in the power industry have become increasingly important. The electricity carbon emission factor is a key parameter for measuring the carbon intensity of electricity consumption and is widely used in emission accounting, load dispatching, and electricity trading. However, traditional carbon factor prediction methods are often static and fail to capture temporal variations, making them inadequate for real-time forecasting needs. This paper proposes a carbon factor prediction model based on temporal convolutional networks and attention mechanisms to enhance prediction accuracy and temporal adaptability. The model incorporates a lightweight gated attention mechanism to enhance responsiveness to short-term external disturbances by weighting meteorological features. In addition, a sliding window approach is employed to construct multi-step time series samples, integrating periodicity, lag, and trend features to improve the model’s capability in capturing dynamic patterns. To optimize model performance, Bayesian Optimization and the Marine Predators Algorithm are used for automatic hyperparameter tuning. Experimental results demonstrate that the proposed method outperforms existing models in both prediction accuracy and generalization ability, providing effective technical support for low-carbon scheduling and precise management in high energy-consuming enterprises.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0287652
- OA Status
- gold
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413152278
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413152278Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1063/5.0287652Digital Object Identifier
- Title
-
Prediction of daily electricity carbon emission factors for high energy-consuming enterprises based on temporal feature optimization and hybrid intelligent algorithmsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-08-01Full publication date if available
- Authors
-
Kefei Guan, Dexin Tan, Qiuzi Zhong, Pei Li, J. Zhong, Wei Cao, Hongyan Zhang, Chong LiangList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0287652Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1063/5.0287652Direct OA link when available
- Concepts
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Electricity, Computer science, Feature (linguistics), Energy (signal processing), Optimization algorithm, Carbon fibers, Algorithm, Artificial intelligence, Mathematical optimization, Engineering, Mathematics, Electrical engineering, Philosophy, Linguistics, Composite number, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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24Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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