Prediction of Steam Consumption in the Tobacco Industry's Primary Processing Workshop

To achieve precise energy supply and energy-efficient operation of the steam system in the primary processing workshop of tobacco enterprises, a Random Forest prediction model is introduced in this study to forecast steam consumption.A correlation analysis is conducted to identify the factors that significantly impact steam load.These key features are then used to develop the Random Forest prediction model, with appropriate hyper parameters selected to enhance prediction accuracy.The results indicate that the Random Forest model offers high prediction precision and strong practical applicability, providing a basis for optimizing the scheduling of the steam system.