Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review
Abstract
:1. Introduction
- RQ1: How can Machine Learning improve energy management in Smart Buildings?
- RQ2: How can Machine Learning help find the building patterns or load profiles?
- RQ3: How can Machine Learning and Data Science be used in Digital Twins to improve energy management?
- RQ4: How can Machine Learning and Data Science build a Digital Twin representative of the energy management of a building?
- RQ5: How does monitoring with the sensors of the Smart Building help provide better building management?
- RQ6: How could a Smart Building contribute to a Smart Urban District or a Smart City?
2. Methods
3. Results
3.1. White-Box, Black-Box, and Gray-Box
3.2. Sensor-Based Monitoring in Buildings
3.3. Real-World Applications
3.4. Model Predictive Control
3.5. Operation and Maintenance
3.6. Heating, Ventilation, and Air Conditioning Systems
3.7. Solar Photovoltaic (PV)
3.8. Smart City and Urban District
3.9. Privacy Issues
3.10. Trends, Techniques and Tools
4. Discussion, Research Gaps, and Opportunities
4.1. Discussion
4.2. Research Gaps and Opportunities
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
BEMS | Building Energy Management Systems |
BIM | Building Information Modeling |
BN | Bayesian network |
CO2 | Carbon dioxide |
CONSTRUCT | Institute of R&D in Structures and Construction |
CPS | Cyber-physical Systems |
DBL | Digital Building Logbook |
DNN | Deep Neural Network |
SVR | Support Vector Regressor |
CNN | Convolutional neural network |
eQuest | Quick Energy Simulation Tool |
FEUP | Faculty of Engineering—University of Porto |
GP-OP | Gaussian process-based Bayesian optimization |
HVAC | Heating, Ventilation, and Air Conditioning |
HVACDT | Digital Twin of the HVAC system |
INESC TEC | Institute for Systems and Computer Engineering, Technology and Science |
IoT | Internet of Things |
LCC | Life-cycle cost |
LIACC | Artificial Intelligence and Computer Science Laboratory |
LSTM | Long short-term memory |
ML | Machine Learning |
MOGA | Multiobjective optimization algorithm |
MPC | Model Predictive Control |
NILM | Non-intrusive load monitoring |
O&M | Operations and Maintenance |
PMV | Predicted Mean Value |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
PV | Photovoltaic |
R | Coefficient correlation |
RF | Random Forest |
SCADA | Supervisory Control and Data Acquisition |
UBEM | Urban building energy methods |
UN | United Nations |
WSN | Wireless Sensor Networks |
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Target | Studies |
---|---|
Data centers | [46] |
Indoor temperature | [25,46,47,48,49] |
Indoor humidity | [50] |
Air quality | [51,52] |
Occupant comfort | [47,53] |
Occupant behavior | [41] |
Operation and maintenance (O&M) | [7,8,11,19,23,34,44,52,53,54,55,56,57,58] |
Cooling towers | [59] |
Solar chimney | [60] |
Solar PV | [49,61] |
Plate solar collector field | [62] |
Heating, ventilation, and air conditioning (HVAC) | [8,12,28,31,47,48,49,53,56,57,63,64] |
Typology of Building | Studies |
---|---|
Office buildings | [26,28,30,43,64] |
Residential buildings | [25,41,47,49,63,65] |
Educational institutions | [12,32,45,48] |
Hospital | [66] |
Museum | [29] |
Theater | [29] |
Gymnasium | [11] |
Algorithm | Application |
---|---|
Deep Neural Network (DNN) | |
Support Vector Regressor (SVR) | |
Random Forest | |
Tree-Based Algorithms | |
Extra Trees |
|
ANN |
|
Convolutional neural network (CNN) | |
LSTM |
Tool | Application | Approach |
---|---|---|
MATLAB | Digital Twin, dashboard and data visualization [25,62,68] | Black-box |
Simulink in MATLAB | Digital Twins (Smart Residential Buildings, HVAC system, and solar PV) [22,24,56,68] | Black-box Gray-box |
TRNSYS | Digital Twins with detailed physical characteristics [9,10,24,25,30] | White-box |
Energy Plus | Digital Twins with detailed physical characteristics [9,10,24,30,64] | White-box |
DesignBuilder | Digital Twins with detailed physical characteristics. Data acquisition concerning the physical characteristics of the building [9,10,17] | White-box |
Dymola | Digital Twins with detailed physical characteristics [9] | White-box |
DOE-2 | Digital Twins with detailed physical characteristics [9] | White-box |
eQuest | Simulation of building envelope characteristics [22] | Gray-box White-box |
Modelica | Digital Twins with detailed physical characteristics [31,67] | White-box |
FMPy | Package in Python: to import data from Modelica to Python [31] | Gray-box |
MLE+ | The link between Energy Plus and Python [24,37] | Gray-box |
pyEp | The link between Energy Plus and Python [24] | Gray-box |
SCADA | For data acquisition [24] | Black-box |
BEMS | For data acquisition [24,42,56] | Black-box |
Autodesk Tandem | Digital Twins with detailed physical characteristics [7,42] | White-box |
Autodesk Revit | Digital Twins with detailed physical characteristics [10,22,41,42,56,57] | White-box |
Dynamo | Historical sensor data integration [42,53] | Gray-box White-box |
Unity 3D | Digital Twins with detailed physical characteristics [11] | White-box |
FIWARE | Supports the Urban Digital Twins of a Smart City or district and is compatible with NGSI-LD, an open standard data format [45] | Black-box Gray-box |
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Palley, B.; Poças Martins, J.; Bernardo, H.; Rossetti, R. Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review. Urban Sci. 2025, 9, 202. https://doi.org/10.3390/urbansci9060202
Palley B, Poças Martins J, Bernardo H, Rossetti R. Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review. Urban Science. 2025; 9(6):202. https://doi.org/10.3390/urbansci9060202
Chicago/Turabian StylePalley, Bruno, João Poças Martins, Hermano Bernardo, and Rosaldo Rossetti. 2025. "Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review" Urban Science 9, no. 6: 202. https://doi.org/10.3390/urbansci9060202
APA StylePalley, B., Poças Martins, J., Bernardo, H., & Rossetti, R. (2025). Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review. Urban Science, 9(6), 202. https://doi.org/10.3390/urbansci9060202