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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2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6449 |
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| author | Kezhi Tu Yanfeng Wang Xian Li Xiangxi Wang Zhenzhong Hu Bo Luo Liu Shi Minghan Li Guangqian Luo Hong Yao |
| author_facet | Kezhi Tu Yanfeng Wang Xian Li Xiangxi Wang Zhenzhong Hu Bo Luo Liu Shi Minghan Li Guangqian Luo Hong Yao |
| author_sort | Kezhi Tu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eeeedacfa9084ca8b9cab9221901309f |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-eeeedacfa9084ca8b9cab9221901309f2025-08-20T02:00:27ZengMDPI AGEnergies1996-10732024-12-011724644910.3390/en17246449CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna FrameworkKezhi Tu0Yanfeng Wang1Xian Li2Xiangxi Wang3Zhenzhong Hu4Bo Luo5Liu Shi6Minghan Li7Guangqian Luo8Hong Yao9State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaGuoneng Yongfu Power Generation Co., Ltd., Guilin 541805, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaState Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaAs 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.https://www.mdpi.com/1996-1073/17/24/6449CO<sub>2</sub> emissioncoal-fired power plantdeep forestRFEOptuna |
| spellingShingle | Kezhi Tu Yanfeng Wang Xian Li Xiangxi Wang Zhenzhong Hu Bo Luo Liu Shi Minghan Li Guangqian Luo Hong Yao CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework Energies CO<sub>2</sub> emission coal-fired power plant deep forest RFE Optuna |
| title | CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework |
| title_full | CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework |
| title_fullStr | CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework |
| title_full_unstemmed | CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework |
| title_short | CO<sub>2</sub> Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework |
| title_sort | co sub 2 sub emission prediction for coal fired power plants by random forest recursive feature elimination deep forest optuna framework |
| topic | CO<sub>2</sub> emission coal-fired power plant deep forest RFE Optuna |
| url | https://www.mdpi.com/1996-1073/17/24/6449 |
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