A tracking control method for electricity-carbon emission forecasting
This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models...
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| Published in | Heliyon Vol. 10; no. 17; p. e36576 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
15.09.2024
Elsevier |
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| Abstract | This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.
•Proposes a carbon emission prediction algorithm based on tracking control.•Proposes an idea of correcting the tracking forecast according to the power data.•Quickly predict future carbon emissions on the basis of ensuring the stability.•Significantly improve the accuracy of the forecast. |
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| AbstractList | This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.
•Proposes a carbon emission prediction algorithm based on tracking control.•Proposes an idea of correcting the tracking forecast according to the power data.•Quickly predict future carbon emissions on the basis of ensuring the stability.•Significantly improve the accuracy of the forecast. This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies. |
| ArticleNumber | e36576 |
| Author | Jin, Lu Li, Jianfeng Cui, Xiaorui Yu, Yaoxian Guo, Yi Chen, Hongyin Wang, Songcen Li, Dezhi |
| Author_xml | – sequence: 1 givenname: Hongyin surname: Chen fullname: Chen, Hongyin email: ssrga163831@sina.com organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 2 givenname: Songcen surname: Wang fullname: Wang, Songcen organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 3 givenname: Jianfeng surname: Li fullname: Li, Jianfeng organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 4 givenname: Yaoxian surname: Yu fullname: Yu, Yaoxian organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 5 givenname: Dezhi surname: Li fullname: Li, Dezhi organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 6 givenname: Lu surname: Jin fullname: Jin, Lu organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 7 givenname: Yi surname: Guo fullname: Guo, Yi organization: National Key Laboratory of Power Grid Safety, Beijing, 100192, China – sequence: 8 givenname: Xiaorui surname: Cui fullname: Cui, Xiaorui organization: State Grid Shanxi Electric Power Research Institute, Taiyuan, Shanxi, China |
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| Keywords | Carbon emission LSTM Forcast Tracking control ARDL |
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