| Issue |
E3S Web Conf.
Volume 654, 2025
Energy and Sustainability Conference (ESC2025)
|
|
|---|---|---|
| Article Number | 04009 | |
| Number of page(s) | 8 | |
| Section | Urban Sustainability and Smart Cities | |
| DOI | https://doi.org/10.1051/e3sconf/202565404009 | |
| Published online | 21 October 2025 | |
Fog-based AI image analysis for load disaggregation using Random Forest and XGBoost
1 Computer Science Department, EnTReC Technical University of Cluj-Napoca
2 Automation Department, EnTReC Technical University of Cluj-Napoca
3 Electrotechnics and Measurement Department, EnTReC Technical University of Cluj-Napoca
* Corresponding author: Alexandru.Berciu@campus.utcluj.ro
The use of artificial intelligence to energy systems is growing, and image-based analysis is showing promise as a tool to improve optimization and monitoring. This paper presents a new load disaggregation architecture that improves responsiveness and efficiency by combining fog computing with artificial intelligence-driven image recognition approaches. To categorize consumption profiles the system examines visual depictions of energy data. Two different kinds of consumption profiles were identified and differentiated using Random Forest and XGBoost classifiers, allowing precise energy usage disaggregation in complex contexts.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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