| Issue |
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
|
|
|---|---|---|
| Article Number | 00144 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202568000144 | |
| Published online | 19 December 2025 | |
Smart Digital Twin for Energy Efficiency in Buildings Using BIM, IoT and AI: Case Study of Villa in Morocco
1 Laboratory of Solid State Physics, Department of Physics, Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
2 National School of Applied Sciences, Abdelmalek Essaadi University, Al Hoceima, Morocco
3 ENSG, UMR 7359-GeoRessources, University of Lorraine, Nancy, France
* Corresponding author: fatimazahrae.saaidi1@gmail.com
This work outlines the creation of an intelligent digital twin for residential Villa that integrates Building Information Modeling (BIM), the Internet of Things (IoT), and Artificial Intelligence (AI) to improve building energy efficiency. A comprehensive 3D model was developed using Revit 2024.3, allowing for solar and energy simulations through Insight and DesignBuilder. Real-time environmental data,such as temperature, humidity, lighting, and occupancy, were gathered via IoT sensors and analyzed using machine learning algorithms to forecast energy consumption patterns and identify anomalies.
Based on these insights, automated control strategies for HVAC and lighting systems were implemented to enhance comfort and reduce energy waste. The proposed framework offers a dynamic, scalable, and regulation-compliant solution for smart energy management in Moroccan buildings. Overall, the developed digital twin showcases the practical potential of integrating BIM, IoT, and AI to achieve sustainable and autonomous building operations.
Key words: BIM / IoT / Artificial Intelligence / Energy Efficiency / Digital Twin / Revit Insight / Smart Buildings
© 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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