Digital Twin-Enabled Framework for Intelligent Monitoring and Anomaly Detection in Multi-Zone Building Systems
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| Veröffentlicht in: | Buildings vol. 15, no. 22 (2025), p. 4030-4054 |
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| 022 | |a 2075-5309 | ||
| 024 | 7 | |a 10.3390/buildings15224030 |2 doi | |
| 035 | |a 3275507207 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231437 |2 nlm | ||
| 100 | 1 | |a Faeze, Hodavand |u Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran; f.hodavand@email.kntu.ac.ir (F.H.); sadeghi@kntu.ac.ir (N.S.) | |
| 245 | 1 | |a Digital Twin-Enabled Framework for Intelligent Monitoring and Anomaly Detection in Multi-Zone Building Systems | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Accuracy | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Data processing | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Buildings | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Optimization | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Fault detection | ||
| 653 | |a Energy consumption | ||
| 653 | |a Robustness | ||
| 653 | |a Internet of Things | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Digital twins | ||
| 653 | |a Sensors | ||
| 653 | |a Support vector machines | ||
| 653 | |a HVAC | ||
| 653 | |a Algorithms | ||
| 653 | |a Complexity | ||
| 653 | |a Anomalies | ||
| 653 | |a Building management systems | ||
| 653 | |a Real time | ||
| 653 | |a Building information modeling | ||
| 653 | |a Forecasting | ||
| 653 | |a Cultural heritage | ||
| 653 | |a Predictive maintenance | ||
| 700 | 1 | |a Issa, Ramaji |u School of Engineering, Construction, and Computing, Roger Williams University, SE1117, One Old Ferry Road, Bristol, RI 02809, USA | |
| 700 | 1 | |a Sadeghi Naimeh |u Faculty of Civil Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran; f.hodavand@email.kntu.ac.ir (F.H.); sadeghi@kntu.ac.ir (N.S.) | |
| 700 | 1 | |a Sarmad, Zandi Goharrizi |u School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran; sarmadzandi@ut.ac.ir | |
| 773 | 0 | |t Buildings |g vol. 15, no. 22 (2025), p. 4030-4054 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275507207/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3275507207/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275507207/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |