Կազմի պատկեր

Digital Twin-Enabled Framework for Intelligent Monitoring and Anomaly Detection in Multi-Zone Building Systems

Պահպանված է:
Հրատարակված է:Buildings vol. 15, no. 22 (2025), p. 4030-4054
Հիմնական հեղինակ: Faeze, Hodavand
Այլ հեղինակներ: Issa, Ramaji, Sadeghi Naimeh, Sarmad, Zandi Goharrizi
Հրապարակվել է:
MDPI AG
Խորագրեր:
Accuracy
Comparative analysis
Data processing
Datasets
Deep learning
Buildings
Energy efficiency
Optimization
Monitoring
Machine learning
Fault detection
Energy consumption
Robustness
Internet of Things
Artificial intelligence
Digital twins
Sensors
Support vector machines
HVAC
Algorithms
Complexity
Anomalies
Building management systems
Real time
Building information modeling
Forecasting
Cultural heritage
Predictive maintenance
Առցանց հասանելիություն:Citation/Abstract
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Համառոտագրություն:The growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. Despite growing interest, key challenges remain, including the neglect of short- and long-term forecasting across different scenarios, insufficiently robust data preparation, and the rare validation of models on multi-zone buildings over extended test periods. To address these gaps, this study presents a comprehensive DT-enabled framework for predictive monitoring and anomaly detection, validated in a multi-zone educational building in Rhode Island, USA, using a full year of operational data for validation. The proposed framework integrates a robust data processing pipeline and a comparative analysis of machine learning models, including LSTM, RNN, GRU, ANN, XGBoost, and RF, to forecast short-term (1 h) and long-term (24 h) indoor temperature variations. The LSTM model consistently outperformed other methods, achieving R2 > 0.98 and RMSE < 0.55 °C for all tested rooms. For real-time anomaly detection, we applied the hybrid LSTM–Interquartile Range (IQR) method on one-step-ahead residuals, which successfully identified anomalous deviations from expected patterns. The model’s predictions remained within a ±1 °C error margin for over 90% of the test data, providing reliable forecasting up to 16 h ahead. This study contributes a validated, generalizable DT methodology that addresses key research gaps, offering practical tools for predictive maintenance and operational optimization in complex building environments.
ISSN:2075-5309
DOI:10.3390/buildings15224030
Աղբյուր:Engineering Database