Assessing the Significance of Machine Learning in Forecasting Energy Recovery from Waste

Abstract

Accurate estimating energy recovery from trash is vital for optimizing the Waste-to-Energy (WTE) mechanisms essential in tackling global waste management processes and energy sustainability issues. This paper analyzes a synthetically expanded dataset generated with the help of SMOTE techniques to compare the performance of four machine learning (ML) models, Decision Tree Regression, Random Forest Regression, CatBoost Regression, and XGBoost Regression. Here, the dataset contains important waste parameters like composition, moisture content, and treatment procedures that help the models forecast energy output with high precision—datasets and evaluate additional machine learning techniques to boost prediction accuracy in industrial WTE systems further. Performance indicators like MAE, RMSE, MAPE, and R² scores have been assessed here to identify each model’s accuracy and computational efficiency. The final result of the analysis states that the ensemble based models, more precisely XG-Boost and CatBoost, outperformed the simpler ones like Decision Tree, where CatBoost achieved the best R² value of 0.9893 and the minimum MAPE of 12.90 percent. Though using little extra storage, CatBoost showed great performance. The obtained results bring useful insights into efficient model selection for WTE applications. Further studies shall therefore be exclusively focused on validating this study’s results in real life conditions.