Advanced Ensemble Learning Approaches Based Energy Consumption

Energy consumption optimization stands as a vital task for the sustainable management of energy resources. Energies usage prediction accuracy leads both to operational cost reductions and improved operational efficiencies. Researchers undertook this study to determine how ensemble learning techniques work for projecting energy consumption. The Energy Consumption Prediction Dataset receives four ensemble analyzes from Random Forest, XGBoost and LightGBM and AdaBoost. The chosen approach for model assessment includes Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Precision, Recall, F1-score, and Training Time which determine the most efficient method. The experimental outcomes show that LightGBM proved superior to all competing models because it yielded the best MAE (0.70) and RMSE (1.05) while reaching 93.0% accuracy within 2.4 seconds. The predictive results from XGBoost remained very competitive alongside its other counterparts. Random Forest and AdaBoost offered inferior accuracy results and required longer training times than the chosen approach. The research demonstrates that boosting approaches provide the best results in energy forecasting since they effectively identify complex patterns.