Research on Application of Digital Twin for Collecting Data to Manage Building’s Energy
Corressponding author's email:
thebao@hcmut.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1910Keywords:
Digital Twin, IoT, Energy simulation, Energy Plus, Real-time data acquisitionAbstract
This study presents a possible affordable digital twin for real-time energy monitoring and model calibration in buildings. Sensors measuring temperature, humidity, voltage, current, and power factor were installed in a small office. Data was transmitted every 20 seconds through the Internet of Things (IoT) using Microsoft Azure platform, then stored in a cloud database. This dataset was used to calibrate an EnergyPlus simulation model of a fixed-capacity air-conditioning system. Calibration followed the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14 for hourly data, using root mean square error, its coefficient of variation, and normalized mean bias error. After four iterations, the model achieved 10,65% and –2,05% for the two key metrics, within the recommended thresholds. The results confirm the value of high-frequency real-time data in improving simulation accuracy and supporting advanced functions such as anomaly detection and predictive control in smart building energy management.
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