<![CDATA[Household energy consumption has become an important issue due to the increasing global energy demand and the need for efficient energy management. Accurate prediction of household energy consumption can support energy planning, reduce energy waste, and improve decision-making in residential energy management. However, developing prediction models that achieve both high predictive performance and interpretability remains a challenging task. Therefore, this study aims to optimize household energy consumption prediction by integrating Ensemble Machine Learning, Feature Selection, and Explainable Artificial Intelligence (XAI). The proposed framework follows the Cross-Industry Standard Process for Data Mining (CRISP-DM), comprising business understanding, data understanding, data preparation, modeling, evaluation, and explainability analysis. The dataset used in this study contains 90,000 household energy consumption records. Feature selection was performed using XGBoost feature importance, while Random Forest, XGBoost, Gradient Boosting, Voting Regressor, and Stacking Regressor were employed as predictive models. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and 10-fold cross-validation. The experimental results indicate that XGBoost achieved the best performance with an MAE of 0.5108, RMSE of 0.6725, MAPE of 0.0607, and R² of 0.9852. Furthermore, SHAP analysis revealed that Peak_Hours_Usage_kWh and Household_Size were the most influential features affecting household energy consumption. In conclusion, the integration of Ensemble Machine Learning, Feature Selection, and XAI effectively yields an accurate, robust, and interpretable model for predicting household energy consumption.]]>
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