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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Bibliographic Details
Published inHeliyon Vol. 10; no. 17; p. e36576
Main Authors Chen, Hongyin, Wang, Songcen, Li, Jianfeng, Yu, Yaoxian, Li, Dezhi, Jin, Lu, Guo, Yi, Cui, Xiaorui
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 15.09.2024
Elsevier
Subjects
ARDL
Carbon emission
Forcast
LSTM
Tracking control
Carbon emission
LSTM
Forcast
Tracking control
ARDL
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Summary: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.
ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e36576