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Article | Green Intelligent Systems and Applications | Volume 5 - Issue 2 - 2025 | 181-190 | https://doi.org/10.53623/gisa.v5i2.910
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Design an Electricity Consumption Prediction Information System Using the Monte Carlo-Based Regression Tree Method

Author(s): Junira Merrylin Ng 1 , Genrawan Hoendarto 2 , Thommy Willay 1
Author(s) information:
1 Departement of Information System, Universitas Widya Dharma Pontianak, Jl. H.O.S. Cokroaminoto No. 445, Darat Sekip, Pontianak Kota, Pontianak, Kalimantan Barat 78243, Indonesia
2 Departement of Informatics, Universitas Widya Dharma Pontianak, Jl. H.O.S. Cokroaminoto No. 445, Darat Sekip, Pontianak Kota, Pontianak, Kalimantan Barat 78243, Indonesia

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Green Intelligent Systems and Applications

Volume 5 - Issue 2 - 2025

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Electricity became an essential component in every industry and was widely used in organizations and households. Improper handling of electricity consumption resulted in unnecessary energy loss and increased costs. The objective of this study was to develop an online electricity consumption prediction information system that was efficient, reliable, and capable of rapid forecasting. The system used IoT sensor data from Universitas Widya Dharma Pontianak, and the Monte Carlo based Regression Tree (MCRT) method was employed to mitigate the unpredictability of the data. Feature selection was conducted using Monte Carlo simulation to identify the most important features, which in this case were the year, month, and day, and these were used in the regression tree model. The developed system was able to provide estimations of hourly and daily energy consumption and the associated costs based on the MCRT model. The MCRT model predicted daily energy consumption with an accuracy of 91.61%, outperforming the Monte Carlo simulation (85.39%) and the Regression Tree method (84.29%). The results demonstrated that the MCRT model was the most efficient in capturing non-linear relationships and regression patterns in the energy consumption data. The constructed system featured an easy-to-use web interface that captured real-time data inputs and visualized predicted consumption for operational use. The system was suitable for public and private sectors, as well as educational and household applications. This approach improved effectiveness in energy management and streamlined resource allocation decision-making. The study highlighted the potential of integrating the Internet of Things (IoT) with predictive analytics to provide actionable, reliable, and precise energy management and monitoring services.

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SUBMITTED: 20 November 2025
ACCEPTED: 10 December 2025
PUBLISHED: 16 December 2025
SUBMITTED to ACCEPTED: 21 days
DOI: https://doi.org/10.53623/gisa.v5i2.910

Cite this article
Ng, J. M. ., Hoendarto, G. ., & Willay, T. . (2025). Design an Electricity Consumption Prediction Information System Using the Monte Carlo-Based Regression Tree Method. Green Intelligent Systems and Applications, 5(2), 181–190. https://doi.org/10.53623/gisa.v5i2.910
Keywords
Electricity consumption prediction Monte Carlo simulation regression tree Internet of Things web-based information system
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