CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework

CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework

As the greenhouse effect intensifies, China faces pressure to manage CO<sub>2</sub> emissions. Coal-fired power plants are a major source of CO<sub>2</sub> in China. Traditional CO<sub>2</sub> emission accounting methods of power plants are deficient in computatio...

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Bibliographic Details
Main Authors: Kezhi Tu, Yanfeng Wang, Xian Li, Xiangxi Wang, Zhenzhong Hu, Bo Luo, Liu Shi, Minghan Li, Guangqian Luo, Hong Yao
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
Subjects:
CO<sub>2</sub> emission
coal-fired power plant
deep forest
RFE
Optuna
Online Access:https://www.mdpi.com/1996-1073/17/24/6449
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Summary:As the greenhouse effect intensifies, China faces pressure to manage CO<sub>2</sub> emissions. Coal-fired power plants are a major source of CO<sub>2</sub> in China. Traditional CO<sub>2</sub> emission accounting methods of power plants are deficient in computational efficiency and accuracy. To solve these problems, this study proposes a novel RF-RFE-DF-Optuna (random forest–recursive feature elimination–deep forest–Optuna) framework, enabling accurate CO<sub>2</sub> emission prediction for coal-fired power plants. The framework begins with RF-RFE for feature selection, identifying and extracting the most important features for CO<sub>2</sub> emissions from the power plant, reducing dimensionality from 46 to just 5 crucial features. Secondly, the study used the DF model to predict CO<sub>2</sub> emissions, combined with the Optuna framework, to enhance prediction accuracy further. The results illustrated the enhancements in model performance and showed a significant improvement with a 0.12706 increase in R<sup>2</sup> and reductions in MSE and MAE by 81.70% and 36.88%, respectively, compared to the best performance of the traditional model. This framework improves predictive accuracy and offers a computationally efficient real-time CO<sub>2</sub> emission monitoring solution in coal-fired power plants.
ISSN:1996-1073

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