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Bubu Bukhori Muslim
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : journal of intelligent systems technology and informatics

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Optimizing Daily Household Energy Consumption Prediction Using Ensemble Machine Learning, Feature Selection, and Explainable Artificial Intelligence Bubu Bukhori Muslim; Jujun Munawar
Journal of Intelligent Systems Technology and Informatics Vol 2 No 2 (2026): JISTICS, July 2026
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v2i2.224

Abstract

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.
Co-Authors Adam Ramdan Ai Hilma Khoiriyah Ai Nur Azizah Arsil Muhammad Harits Asep Deddy Supriatna Bintang Maulida Fazriani A Dzakwan Fauzan Padlulloh Faiz Pratama Hisyam Eka Pramudita Jujun Munawar Kalma Juniya Farid M. Gilar Fatih Nurohman Muhamad Chikal Ubaidilah Nurhasan Muhammad Fahmi Faisal Muhammad Ilham Dede Saputra Nazwa Nurazizah Rifki Ahmad Dzulfikri Rizki Abdul Hadi Siti Fadillah Kamilah Siti Komalasari Soni Irawan Vito Gunawan Windi Setia Lestari