CCPP Power Prediction Using CatBoost with Domain Knowledge and Recursive Feature Elimination

Combined cycle power plants are modern power generation systems that provide an efficient and environmentally friendly way of generating electricity. With the development of smart grids, higher requirements have been put forward for their power prediction. Using a dataset comprising 9568 observations from a combined cycle power plant operating at full load for 6 years, a high-precision power prediction model integrating CatBoost and domain knowledge is proposed. Twenty new features were designed based on domain expertise, and Recursive Feature Elimination was applied to select the most informative features, optimizing model performance. Experimental results demonstrate that CatBoost outperformed six commonly used machine learning algorithms, both with and without domain knowledge integration. And the incorporation of domain knowledge improved the predictive performance of all evaluated models, underscoring the effectiveness and general applicability of the proposed features. Moreover, Recursive Feature Elimination was applied to select 11 features. The optimized CatBoost model achieved the best predictive accuracy with a root mean square error of 2.8545, a mean absolute error of 1.9645, and an R-squared of 0.9702. A comparative analysis with existing literature methods further validated the superior performance of the proposed approach. These findings highlight the effectiveness of integrating domain knowledge with machine learning and its potential for improving power output prediction in combined cycle power plants.