CO 2 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 CO₂ emissions. Coal-fired power plants are a major source of CO₂ in China. Traditional CO₂ 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 CO₂ emission prediction for coal-fired power plants. The framework begins with RF-RFE for feature selection, identifying and extracting the most important features for CO₂ emissions from the power plant, reducing dimensionality from 46 to just 5 crucial features. Secondly, the study used the DF model to predict CO₂ 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 R² 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 CO₂ emission monitoring solution in coal-fired power plants. [ABSTRACT FROM AUTHOR]