Household Energy Consumption Prediction Using Ensemble Machine Learning Models: A Comparative Analysis

Edris Shamsulhaq; Fikri Arif Wicaksana

doi:10.32528/elkom.v8i2.6418

Authors

Edris Shamsulhaq Nusa Putra University
Fikri Arif Wicaksana Nusa Putra University

DOI:

https://doi.org/10.32528/elkom.v8i2.6418

Keywords:

Prediksi Energi Rumah Tangga, Pembelajaran Ansambel, Random Forest, Kepentingan Fitur, Jaringan Cerdas

Abstract

Accurate prediction of household energy consumption plays a vital role in smart grid management, demand-side response, and energy efficiency planning. This study presents a comparative analysis of four ensemble machine learning models—Random Forest, XGBoost, LightGBM, and CatBoost—for predicting household active power consumption. The publicly available Household Electric Power Consumption dataset from the UCI repository, comprising over two million measurements, was used for experimentation. Three feature scenarios were designed to evaluate model performance under different input conditions: using all features, excluding current intensity, and excluding both intensity and reactive power. Results show that Random Forest achieved the highest predictive accuracy with an R² of 0.913 in the reduced-feature scenario, outperforming gradient boosting alternatives. Statistical validation using the Wilcoxon signed-rank test was used to assess the effect of feature exclusion across the three scenarios. The study demonstrates that reliable energy prediction remains achievable using sub-metering, voltage, and temporal features, offering a practical baseline for smart meter applications.

References

T. M. Olatunde, A. C. Okwandu, D. O. Akande, and E. IJSTRA, “Reviewing the impact of energy-efficient appliances on household consumption,” International Journal of Science and Technology Research Archive, vol. 6, no. 2, pp. 001–011, Apr. 2024, doi: 10.53771/IJSTRA.2024.6.2.0038.

A. Bandyopadhyay and A. Bhattacharya, “Residential Appliance Usage Patterns From Overall Energy Consumption Data: A Statistical Machine Learning Approach,” ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 8A-2021, Jan. 2022, doi: 10.1115/IMECE2021-70122.

T. M. Ghazal, J. I. Janjua, W. Abushiba, M. Ahmad, T. Abbas, and K. Ateeq, “Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction,” International Conference on Electrical and Computer Engineering Researches, ICECER 2024, 2024, doi: 10.1109/ICECER62944.2024.10920448.

K. Okoli and Y. A. Bekeneva, “Accurate Neural Prophecy for Short-Term Load Forecasting of Optimal Renewable Energy,” Proceedings of 2023 5th International Conference on Control in Technical Systems, CTS 2023, pp. 196–198, 2023, doi: 10.1109/CTS59431.2023.10288952.

Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3125–3148, May 2019, doi: 10.1109/TSG.2018.2818167.

S. M. Qaisar, D. A. Bashawyah, F. Alsharif, and A. Subasi, “A comprehensive review on the application of machine learning techniques for analyzing the smart meter data,” Machine Learning for Sustainable Development, pp. 53–75, Jul. 2021, doi: 10.1515/9783110702514-004/XML.

A. Wong et al., “Machine Learning Models Application in Daily Forecasting of Hourly Electricity Usage,” SysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings, 2022, doi: 10.1109/SYSCON53536.2022.9773835.

M. Tarani and T. Sowjanya, “Smart Forecasting of Power Consumption with Machine Learning Models,” International Scientific Journal of Engineering and Management, vol. 05, no. 07, pp. 1–9, Jul. 2026, doi: 10.55041/ISJEM08278.

P. A. Ujjainkar, T. H. Khan, S. Nirkhi, S. Mudpalliwar, S. Chavan, and K. K. Baraskar, “Energy Consumption Prediction using Ensemble Learning and Streamlit Application,” 2025 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application, ICAIQSA 2025 - Proceedings, 2025, doi: 10.1109/ICAIQSA67794.2025.11440429.

G. Airlangga, “Evaluation of Ensemble and Hybrid Models for Predicting Household Energy Consumption: A Comparative Study of Machine Learning Approaches,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 8, no. 1, pp. 94–101, Dec. 2025, doi: 10.24014/IJAIDM.V8I1.32819.

I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.

Er. M. Kaur, Er. G. Kaur, Dr. R. Rana, and Mr. S. Sharma, “A Comparative Study of XGBoost and Random Forest for Modern Machine Learning Applications,” International Journal of Scientific Research in Engineering and Management, vol. 09, no. 12, pp. 1–9, Dec. 2025, doi: 10.55041/IJSREM55183.

G. S. Ramnath, R. Harikrishnan, S. M. Muyeen, and K. Kotecha, “Household electricity consumption prediction using database combinations, ensemble and hybrid modeling techniques,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-57550-9.

D. Attipoe, D. K. Moulla, E. Mnkandla, and A. Abran, “Predicting Residential Energy Consumption in South Africa Using Ensemble Models,” Applied Computational Intelligence and Soft Computing, vol. 2025, no. 1, 2025, doi: 10.1155/acis/5211419.

J. Moon, M. Maqsood, D. So, S. W. Baik, S. Rho, and Y. Nam, “Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence,” PLoS One, vol. 19, no. 11 November, Nov. 2024, doi: 10.1371/journal.pone.0307654.

I. Kiprijanovska, S. Stankoski, I. Ilievski, S. Jovanovski, M. Gams, and H. Gjoreski, “HousEEC: Day-ahead household electrical energy consumption forecasting using deep learning,” Energies (Basel)., vol. 13, no. 10, May 2020, doi: 10.3390/en13102672.

E. Fitri, S. N. Nugraha, and M. Ernawati, “ELECTRICITY CONSUMPTION PREDICTION AND INFLUENTIAL FACTORS ANALYSIS USING MACHINE LEARNING REGRESSION,” JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), vol. 11, no. 4, pp. 1197–1211, May 2026, doi: 10.33480/jitk.v11i4.7914.

P. H. K. Utama et al., “Adaptive Ensemble Learning for Enhancing Building Energy Consumption Prediction: Insights from COVID-19 Pandemic Energy Consumption Dynamics,” Journal of Engineering and Technological Sciences, vol. 57, no. 2, pp. 161–174, Apr. 2025, doi: 10.5614/j.eng.technol.sci.2025.57.2.2.

B. B. Muslim and J. Munawar, “Optimizing Daily Household Energy Consumption Prediction Using Ensemble Machine Learning, Feature Selection, and Explainable Artificial Intelligence,” Journal of Intelligent Systems Technology and Informatics, vol. 2, no. 2, pp. 82–90, Jul. 2026, doi: 10.64878/jistics.v2i2.224.

G. Hebrail and A. Berard, “Individual Household Electric Power Consumption,” 2006, UCI Machine Learning Repository. doi:10.24432/C58K54.