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Predicting electrical power consumption is essential in many industries. This paper presents a method to predict total electrical power consumption in a certain period of Hot Rolling Mill (HRM) in steel mills. We adequately pre-processed operation data, including work schedule, work result, and power measurement. In addition, based on domain knowledge, we derived several useful variables relevant to power consumption. To predict power consumption, we developed a Multi-Level Stacked Regression model that combines the strengths of multiple prediction models. Real-world applications have dataset shift characteristics in which the covariance between features or distribution of the features changes over time due to frequent changes in the working environment. To verify the generalization accuracy of our model, we trained and tested our model for various training/test splits and compared it with several comparative models. Our model showed the best accuracy in all training/test splits. We verified whether the predictive model reflects the real-world environment well by analyzing the actual physical meaning of the essential features used in the predictive model. • A series of pre-processing methods are applied to real data from Hot Rolling Mill. • A Multi-Level Stacked Regression algorithm is proposed. • Performance on various training/test splits that contains dataset shifts is verified. [ABSTRACT FROM AUTHOR]