Prediction of Industrial Boiler Energy Efficiency Using Stacking Ensemble Learning

Abstract Accurate predictions of energy efficiency facilitate the energy-saving renovation industrial boilers. To improve prediction accuracy, this paper proposes an boiler model based on stacking ensemble learning method. The base models, including LR, RF, GBDT, XGBoost, and ANN, are established in study, then method is employed to integrate these models into a strong model. Experimental results indicate that outperforms terms accuracy stability.