Optimization of Energy Consumption Prediction with Random Forest Regressor and XGBoost Feature Importance - Risma Moulidya Syafei; Tiara Lailatul Nikmah; Devi Nurul Anisa; Sidiq Noor Kharisma (Journal of Information System Exploration and Research, 2026)
Energy consumption is increasing as industry and technology advance. However, it will have a bad impact if its use is not properly controlled. Therefore, predicting energy consumption is needed to prevent energy waste and to streamline its use across several influencing factors. Predictions are made using the Random Forest Regressor method. Where regression and Random Forest techniques can produce accurate results for continuous values such as total energy consumption. The feature importance method is also used to select the most influential features. Where of the 40 features in the energy consumption dataset in Southern California, only 24 features were selected based on the average threshold of the gain value. The results showed that the use of XGBoost feature importance lowered the Mean Absolute Error (MAE) value of the Random Forest Regressor, which was 16.56 to 16.55. This value is the difference between the actual data and the predicted data. This proves that the model successfully predicts with a small error value. The application of feature importance in energy consumption prediction using Random Forest Regressor is expected to be more efficient in energy consumption, especially in the sectors that most affect the increase in energy consumption.