Enhanced Prediction of Power Plant Energy Output Using Advanced Ensemble Machine Learning Models

Efficient production of electrical power in combined cycle power stations requires precise prediction of the electrical power output in order to optimize the performance of a power plant, to cut down operational costs, and to stabilize the grid. This paper compares the full-load electrical power output prediction via a selection of several machine learning regression models in a comparative perspective that tries to learn the linked associations between the ambient atmospheric conditions and the full-load electrical power output. We test many models with both conventional methods (Lasso, Ridge, Decision Tree), those involving kernel (multinomial support vectors support vectors), and state of the art ensemble methods (XGBoost, CatBoost, LightGBM, Random Forest) using the Combined Cycle Power Plant (CCPP) dataset. We have significantly improved the results compared to previous studies, with the highest performance being by an XGBoost model, which scored Mean Absolute Error (MAE) 2.41 MW, Root Mean Squared Error (RMSE) 3.37 MW, and an R2 of 0.9615. It is better than the recently benchmarked Bagging REP Tree model already cited in the literature. The analysis of residuals and error distributions in detail confirms the effectiveness and soundness of the best models. Additional information provided by feature correlation analysis demonstrates that ambient temperature and exhaust vacuum have the highest impact on prediction of power output. Such results confirm the advantages of modern ensemble learning approaches to energy forecasting tasks and offer a scalable methodology to be used in a live power plant monitoring system.