Tree-Based Algorithms performance in Predicting Household Energy Consumption

Ferdinand Nathanael; Jimmy Tjen; Genrawan Hoendarto

doi:10.18535/sshj.v9i01.1576

Abstract

Predicting household energy consumption is becoming increasingly important as we strive to manage energy costs and support environmental sustainability. This study takes a close look at how the random forest machine learning method can be used to forecast household energy usage. We used a detailed dataset from the UCI Machine Learning Repository, covering 47 months of minute-by-minute energy consumption data. By comparing Random Forest with other popular machine learning techniques like Gradient Boosting, Regression Tree, Support Vector Machine, and Naïve Bayes, we found that Random Forest stood out for its predictive accuracy, achieving 77.05%. While it does take longer to train, the benefits of accuracy make it a strong candidate for practical energy management solutions. Our findings suggest that Random Forest is particularly well-suited for forecasting household energy needs, providing reliable data that could help optimize energy use and craft effective energy-saving strategies. Looking ahead, future research should aim to improve dataset quality and explore advanced optimization techniques to push prediction accuracy even further.

Keywords

Random ForestPredictionEnergy ConsumptionComparisonMachine LearningAccuracy.

References

IEA, Global Energy Review 2021, Paris: https://www.iea.org/reports/global-energy-review-2021, 2021.Google Scholar ↗
IEA, "Global Energy Stauts Report 2019," https://www.iea.org/reports/global-energy-co2-status-report-2019, Paris, 2019.Google Scholar ↗
M. S. Bakare, A. Abdulkarim, M. Zeeshan and A. N. Shuaibu, "A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction," Energy Informatics, vol. 6, pp. 1-59, 2023.Google Scholar ↗
H. Lund and B. V. Mathiesen, "Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050," Energy, vol. 34, no. 5, pp. 524-531, 2009.Google Scholar ↗
A. Kavousian, R. Rajagopal and M. Fischer, "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, vol. 55, pp. 184-194, 2013.Google Scholar ↗
R. Mathumitha, P. Rathika and K. Manimala, "Intelligent deep learning techniques for energy consumption," Artificial Intelligence Review, vol. 57, p. 35, 2024.Google Scholar ↗
IEA, "World Energy Outlook 2021," https://www.iea.org/reports/world-energy-outlook-2021, Paris, 2021.Google Scholar ↗
DOE, "Energy Savers Guide," https://www.energy.gov/energysaver/energy-saver-guide-tips-saving-money-and-energy-home, 2020.Google Scholar ↗
IEA, "Renewable Energy Market Update - June2023,"https://www.iea.org/reports/renewable-energy-market-update-june-2023, Paris, 2023.Google Scholar ↗
S. Bourhnane, M. R. Abid,, R. Lghoul, K. Zine-Dine, N. Elkamoun and D. Benhaddou, "Machine learning for energy consumption prediction and scheduling in smart buildings," SN Applied Sciences, vol. 2, 2020.Google Scholar ↗
S. S. Aravind, P. Tanna and P. Vittaldas , "Modeling Energy Consumption Using Machine Learning," Frontiers in Manufacturing Technology, vol. 2, 2022.Google Scholar ↗
L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.Google Scholar ↗
P. Nie, M. Roccotelli, M. P. Fanti, Z. Ming and Z. Li, "Prediction of home energy consumption based on gradient boosting regression tree," Energy Report, vol. 7, pp. 1246-1255, 2021.Google Scholar ↗
"Energy consumption prediction by using machine learning for smart building: Case study in Malaysia," Developments in the Built Environment, vol. 5, 2021.Google Scholar ↗
P. Michailidis, I. Michailidis, S. Gkelios and E. Kosmatopoulos, "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, vol. 17, no. 3, 2024.Google Scholar ↗
D. K. Moulla, D. Attipoe, E. Mnkandla and A. Abran, "Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa," Sustainability, vol. 16, no. 11, 2024.Google Scholar ↗
G. Hebrail and A. Berard, "Individual Household Electric Power Consumption," UCI Machine Learning Repository, 2012.Google Scholar ↗

[refR-1] IEA, Global Energy Review 2021, Paris: https://www.iea.org/reports/global-energy-review-2021, 2021.Google Scholar ↗

[refR-2] IEA, "Global Energy Stauts Report 2019," https://www.iea.org/reports/global-energy-co2-status-report-2019, Paris, 2019.Google Scholar ↗

[refR-3] M. S. Bakare, A. Abdulkarim, M. Zeeshan and A. N. Shuaibu, "A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction," Energy Informatics, vol. 6, pp. 1-59, 2023.Google Scholar ↗

[refR-4] H. Lund and B. V. Mathiesen, "Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050," Energy, vol. 34, no. 5, pp. 524-531, 2009.Google Scholar ↗

[refR-5] A. Kavousian, R. Rajagopal and M. Fischer, "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, vol. 55, pp. 184-194, 2013.Google Scholar ↗

[refR-6] R. Mathumitha, P. Rathika and K. Manimala, "Intelligent deep learning techniques for energy consumption," Artificial Intelligence Review, vol. 57, p. 35, 2024.Google Scholar ↗

[refR-7] IEA, "World Energy Outlook 2021," https://www.iea.org/reports/world-energy-outlook-2021, Paris, 2021.Google Scholar ↗

[refR-8] DOE, "Energy Savers Guide," https://www.energy.gov/energysaver/energy-saver-guide-tips-saving-money-and-energy-home, 2020.Google Scholar ↗

[refR-9] IEA, "Renewable Energy Market Update - June2023,"https://www.iea.org/reports/renewable-energy-market-update-june-2023, Paris, 2023.Google Scholar ↗

[refR-10] S. Bourhnane, M. R. Abid,, R. Lghoul, K. Zine-Dine, N. Elkamoun and D. Benhaddou, "Machine learning for energy consumption prediction and scheduling in smart buildings," SN Applied Sciences, vol. 2, 2020.Google Scholar ↗

[refR-11] S. S. Aravind, P. Tanna and P. Vittaldas , "Modeling Energy Consumption Using Machine Learning," Frontiers in Manufacturing Technology, vol. 2, 2022.Google Scholar ↗

[refR-12] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.Google Scholar ↗

[refR-13] P. Nie, M. Roccotelli, M. P. Fanti, Z. Ming and Z. Li, "Prediction of home energy consumption based on gradient boosting regression tree," Energy Report, vol. 7, pp. 1246-1255, 2021.Google Scholar ↗

[refR-14] "Energy consumption prediction by using machine learning for smart building: Case study in Malaysia," Developments in the Built Environment, vol. 5, 2021.Google Scholar ↗

[refR-15] P. Michailidis, I. Michailidis, S. Gkelios and E. Kosmatopoulos, "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, vol. 17, no. 3, 2024.Google Scholar ↗

[refR-16] D. K. Moulla, D. Attipoe, E. Mnkandla and A. Abran, "Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa," Sustainability, vol. 16, no. 11, 2024.Google Scholar ↗

[refR-17] G. Hebrail and A. Berard, "Individual Household Electric Power Consumption," UCI Machine Learning Repository, 2012.Google Scholar ↗