XGBoost-based critical equipment consumption prediction

By analyzing the factors affecting the consumption of critical equipment, dividing the data on the consumption of critical equipment into training set and test set, and using the training set to establish XGBoost regression model to predict the consumption of critical equipment, the calculations show that the average relative error of the XGBoost regression model on the prediction of the training set and the test set are both small, and the prediction effect is better, and we get the influencing factors of the importance, which is of great significance for key equipment protection.