Title
COGNITIVE DIGITAL TWINS FOR SMART BUILDING ENERGY MANAGEMENT: A SYSTEMATIC REVIEW
Authors
Venkatachalam S, Vishnuvardhan K, Raja K, Ramya P, Gowsikan K, Santhiesh V, Bavadharani R V
Abstract
Digital Twin (DT) respect is getting more and more popular in smart building development, the majority of the current DTs are however still limited to static modelling and reactive control. The next generation concept, Cognitive Digital Twins (CDT) is being researched promising to surmount these limitations by implementing AI technology coupled with semantic reasoning and adaptive decision-making to augment autonomous and people-centred building operations. This article systematically reviews research on CDT applied to building performance and physical environment management. A PRISMA guided search of Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink and ACM Digital Library (2010–2025) identified 1,649 records with 104 studies meeting the inclusion criteria. CDTs were analysed in terms of architecture, cognitive functions that CDTs support, learning and reasoning modes, knowledge representation, validation process and application field. The article reveals a rising trend in the utilization of artificial intelligence, machine learning, knowledge graphs, predictive control and reinforcement learning to optimize building energy efficiency, indoor environmental quality, occupant comfort and predictive maintenance. Nevertheless, the majority of CDT solutions are still simulation-oriented, with scarce practical engulfment poor ontology engineering and unlike cognitive frameworks. This paper emphasizes the emerging capabilities of CDTs, yet it also recognizes the central deficiencies in interoperability, semantic modelling and continuous cognitive performance feedback. This article presents a conceptual CDT framework and a research roadmap for scalable smart building systems
Keywords
Cognitive Digital Twins; Smart Building Energy Management; Semantic Modelling; AI Models; Net Zero Buildings
Full Text
References
1. Marzouk, O.A. Summary of the 2023 report of TCEP (tracking clean energy progress) by the International Energy
Agency (IEA), and proposed process for computing a single aggregate rating. in E3S Web of Conferences. 2025. EDP
Sciences.
2. UNEP, U., 2022 global status report for buildings and construction: towards a zero-emission, efficient and resilient
buildings and construction sector. 2022, United Nations Environment Programme Nairobi.
3. Shahid, M.N., M.U. Shahid, and M. Irfan, Advances in Building Energy Management: A Comprehensive Review.
Buildings, 2025. 15(23): p. 4237.
4. Afram, A. and F. Janabi-Sharifi, Theory and applications of HVAC control systems–A review of model predictive
control (MPC). Building and environment, 2014. 72: p. 343–355.
5. Katipamula, S. and M.R. Brambley, Methods for fault detection, diagnostics, and prognostics for building systems—
a review, part I. Hvac&R Research, 2005. 11(1): p. 3–25.
6. Ali, M.I., et al., Cognitive Digital Twins for Smart Manufacturing. IEEE Intelligent Systems, 2021. 36: p. 96–100.
7.Soori, M., B. Arezoo, and R. Dastres, Digital Twin for Smart Manufacturing, A Review. Sustainable Manufacturing
and Service Economics, 2023.
8.Lin, Y., et al., Advancing AI-Enabled Techniques in Energy System Modeling: A Review of Data-Driven, MechanismDriven, and Hybrid Modeling Approaches. Energies, 2025. 18(4): p. 845.
9.Ali, D., V. Motuzienė, and R. Džiugaitė-Tumėnienė, AI-Driven Innovations in Building Energy Management Systems:
A Review of Potential Applications and Energy Savings. Energies, 2024. 17: p. 4277.
10.Gubbi, J., et al., Internet of Things (IoT): A vision, architectural elements, and future directions. Future generation
computer systems, 2013. 29(7): p. 1645–1660.
11.Boje, C., et al., Towards a semantic Construction Digital Twin: Directions for future research. Automation in
construction, 2020. 114: p. 103179.
12.Grieves, M. and J. Vickers, Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex
systems, in Transdisciplinary perspectives on complex systems: New findings and approaches. 2016, Springer. p. 85–
113.
13.Kritzinger, W., et al., Digital Twin in manufacturing: A categorical literature review and classification. IfacPapersOnline, 2018. 51(11): p. 1016–1022.
14.Silvestre, D. and P. Lourençoc, Guidance and Control for In-Orbit Servicing and Assembly Missions. 2024.
15.Balaji, B., et al. Brick: Towards a unified metadata schema for buildings. in Proceedings of the 3rd ACM
International Conference on Systems for Energy-Efficient Built Environments. 2016.
16.Tao, F., et al., Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation
and comparison. Engineering, 2019. 5(4): p. 653–661.
17.Madakam, S., R.M. Holmukhe, and R.K. Revulagadda, The next generation intelligent automation:
hyperautomation. JISTEM-Journal of Information Systems and Technology Management, 2022. 19: p. e202219009.
18.Wang, S. and Z. Ma, Supervisory and optimal control of building HVAC systems: A review. Hvac&R Research,
2008. 14(1): p. 3–32.
19.Chen, G., et al., A Systematic Review of Building Energy Consumption Prediction: From Perspectives of Load
Classification, Data-Driven Frameworks, and Future Directions. Applied Sciences, 2025. 15(6): p. 3086.
20.Killian, M. and M. Kozek, Ten questions concerning model predictive control for energy efficient buildings. Building
and Environment, 2016. 105: p. 403–412.
21.Rawat, A., et al., Advanced digital technologies in the post-disaster reconstruction process—A review leveraging
small language models. Buildings, 2024. 14(11): p. 3367.
22.Ali, M.I., et al., Cognitive digital twins for smart manufacturing. IEEE Intelligent Systems, 2021. 36(2): p. 96–100.
23.Wang, Z., et al., AlphaBuilding ResCommunity: A multi-agent virtual testbed for community-level load coordination.
Advances in Applied Energy, 2021. 4: p. 100061.
24.Nie, J., et al., Digital Twin-based Smart Building Management and Control Framework. DEStech Transactions on
Computer Science and Engineering, 2019.
25.Boukaf, M., F. Fadli, and N. Meskin, A Comprehensive Review of Digital Twin Technology in Building Energy
Consumption Forecasting. IEEE Access, 2024. PP: p. 1–1.
26.Giri, B.R., et al., Advancements in ocular therapy: a review of emerging drug delivery approaches and
pharmaceutical technologies. Pharmaceutics, 2024. 16(10): p. 1325.
27.Rozza, G., G. Stabile, and F. Ballarin, Advanced reduced order methods and applications in computational fluid
dynamics. 2022: SIAM.
28.Flah, A., et al., Advancing Sustainable Energy Transition Through Green Hydrogen Valleys. IEEE Access, 2025.
29.Fuller, A., et al., Digital twin: enabling technologies, challenges and open research. IEEE access, 2020. 8: p.
108952–108971.
30.Alnaser, A., M. Maxy, and H. Elmousalami, AI-Powered Digital Twinsand Internet of Things for Smart Cities and
Sustainable Building Environment. Applied Sciences, 2024. 14: p. 12056.
31.Tao, F., et al., Digital twin-driven product design, manufacturing and service with big data. The International Journal
of Advanced Manufacturing Technology, 2018. 94(9): p. 3563–3576.
32.Kaptanoglu, A., An exploration of data-driven system identification and machine learning for plasma physics. 2021:
University of Washington.
33.Amasyali, K. and N. El-Gohary, Deep Learning for Building Energy Consumption Prediction. 2017.
34.Zhu, B., et al., Achieving efficient uranium extraction by in situ ultrasonic texturization of commercial Fe powder.
Environmental Science: Nano, 2023. 10(8): p. 2201–2210.
35.Agostinelli, S., COGNIBUILD: Cognitive Digital Twin Framework for Advanced Building Management and
Predictive Maintenance. 2023. p. 69–78.
36.Böhnel, H. and A. Rodríguez-Trejo, Comment on García et al. (2021) Semicontinuous paleomagnetic record of the
last 1 Ma from radiometrically dated igneous rocks (Trans-Mexican Volcanic Belt and surrounding areas). Journal of
South American Earth Sciences, 2022. 114: p. 103684.
37.Balderas-Martinez, Y., et al., Semantic Reasoning Using Standard Attention-Based Models: An Application to
Chronic Disease Literature. Big Data and Cognitive Computing, 2025. 9: p. 162.
38.Malik, L., K. Ullah, and M. Soomro, The Impact of Cognitive and Emotional Biases on Individual Investor’s
Investment Decision: Mediating Role of Risk Perception. Pakistan Journal of Humanities and Social Sciences, 2024.
12: p. 2651–2660.
39.Dang, S., et al. A Survey of the Routing Problem for Cooperated Trucks and Drones. Drones, 2024. 8, 550 DOI:
10.3390/drones8100550.
40.Ji, T., J. Polzer, and X. Xu, Cognitive Digital Twin Framework for Smart Manufacturing. 2023. 1–6.
41.Floris, A., et al. An IoT-Based Smart Building Solution for Indoor Environment Management and Occupants
Prediction. Energies, 2021. 14, 2959 DOI: 10.3390/en14102959.
42.Fierro, G., et al. Beyond a house of sticks: Formalizing metadata tags with brick. in Proceedings of the 6th ACM
international conference on systems for energy-efficient buildings, cities, and transportation. 2019.
43.Vamvakas, D., et al., Review and evaluation of reinforcement learning frameworks on smart grid applications.
Energies, 2023. 16(14): p. 5326.
44.Liu, X., et al., Research on the Application of Cloud Edge Collaboration Architecture in Power System. Journal of
Physics: Conference Series, 2024. 2795: p. 012022.
45.Zi, M., The Transition From Cardiac Remodelling to Heart Failure: Using Mouse Models to Explore the Underlying
Mechanisms. 2017: The University of Manchester (United Kingdom).
46.Wetter, M., Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed.
Journal of Building Performance Simulation, 2011. 4(3): p. 185–203.
47.Amangeldy, B., et al., AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital
Twins and LLMs for Proactive Comfort, IEQ, and Energy Management. Sensors, 2025. 25(17): p. 5265.
48.Zheng, X., J. Lu, and D. Kiritsis, The emergence of cognitive digital twin: vision, challenges and opportunities.
International Journal of Production Research, 2022. 60(24): p. 7610–7632.
49.Garcia, D.A.F., et al. Decisioning Workshop 2023. in Decisioning workshop 2023. 2023.
50.Zhu, D., et al., Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems. Frontiers
in neurorobotics, 2024. 18: p. 1385778.
51.Zhang, J. and S. Jiang, Review of artificial intelligence applications in construction management over the last five
years. Engineering, Construction and Architectural Management, 2024.
52.Heinonen, O., Design and implementation of a machine learning-based tool and a robotic process automation
workflow for SAP S/4HANA item setup. 2025.
53.Delgoshaei, P., M. Heidarinejad, and M.A. Austin A Semantic Approach for Building System Operations:
Knowledge Representation and Reasoning. Sustainability, 2022. 14, 5810 DOI: 10.3390/su14105810.
54.Amangeldy, B., et al., A review of artificial intelligence and deep learning approaches for resource management in
smart buildings. Buildings, 2025. 15(15): p. 2631.
55.Martínez-Orts, M. and S. Pujals, Responsive supramolecular polymers for diagnosis and treatment. International
journal of molecular sciences, 2024. 25(7): p. 4077.
56.Widiastono, A., et al., Internet Of Things: Solusi Pintar untuk Dunia Modern. Penerbit Mifandi Mandiri Digital,
2024. 1(01).
57.McHirgui, N., et al. The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive
Review. Applied Sciences, 2024. 14, 10933 DOI: 10.3390/app142310933.
58.Boschert, S. and R. Rosen, Digital twin—the simulation aspect, in Mechatronic futures: Challenges and solutions
for mechatronic systems and their designers. 2016, Springer. p. 59–74.
59.Vaisi, S., et al., A comparison between different machine learning techniques for predicting heating energy
consumption for residential buildings in a cold climate. Energy Efficiency, 2025. 18(7): p. 85.
60.Yao, J.-F., et al., Systematic review of digital twin technology and applications. Visual computing for industry,
biomedicine, and art, 2023. 6: p. 10.
61.Wang, X. and G. Gao. Smarteye: An open source framework for real-time video analytics with edge-cloud
collaboration. in Proceedings of the 29th ACM International Conference on Multimedia. 2021.
62.Ayankojo, A.G., J. Reut, and V. Syritski Electrochemically Synthesized MIP Sensors: Applications in Healthcare
Diagnostics. Biosensors, 2024. 14, 71 DOI: 10.3390/bios14020071.
63.Qiu, L., Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across
Urban Communities. Computer Life, 2025. 13: p. 8–15.
64.Bekal, G.U., A. Ghareeb, and A. Pujari, Continual Reinforcement Learning for HVAC Systems Control: Integrating
Hypernetworks and Transfer Learning. arXiv preprint arXiv:2503.19212, 2025.
65.Shaheen, K., et al., Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks.
Journal of Intelligent & Robotic Systems, 2022. 105(1): p. 9.
66.Prabowo, A., et al., Building Timeseries Dataset: Empowering Large-Scale Building Analytics. Advances in Neural
Information Processing Systems, 2024. 37: p. 133180–133206.
67.Qin, B., et al., Machine and deep learning for digital twin networks: A survey. IEEE Internet of Things Journal,
2024. 11(21): p. 34694–34716.
68.Hu, Z.-Z., Y. Liu, and J.-M. Zhang, The application and development of digital twin in the marine domain. OCEAN
Учредители: Tsinghua University Press, 2025. 1(1): p. 9470001.
69.Haase, J., et al. The IOT mediated built environment: A brief survey. in 2016 IEEE 14th international conference on
industrial informatics (INDIN). 2016. IEEE.
70.de las Morenas, J., L.M. Belmonte, and R. Morales, Designing an AI-driven digital twin architecture for building
energy prediction. Journal of Building Engineering, 2025: p. 113966.
71.Li, Y., et al., Budget-constrained digital twin synchronization and its application on fidelity-aware queries in edge
computing. IEEE Transactions on Mobile Computing, 2024.
72.Liu, B. and C.-H. Chen, An adaptive multihop branch ensemble-based Graph Adaptation Framework with edgecloud orchestration for condition monitoring. IEEE Transactions on Industrial Informatics, 2023. 19(10): p. 10102–
10113.
73.Gasser, U., Interoperability in the digital ecosystem. Available at SSRN 2639210, 2015.
74.Champaney, V., Advanced model order reduction and data-driven technologies enabling physics-augmented digital
twins. 2023, HESAM Université.
75.Manic, M., et al., Building energy management systems: The age of intelligent and adaptive buildings. IEEE
Industrial Electronics Magazine, 2016. 10(1): p. 25–39.
76.Bhaskaran, S. and S. Muthuraman, A Comprehensive Study of Resource Provisioning and Optimization in Edge
Computing. Computers, Materials & Continua, 2025. 83(3).
77.Chinnasamy, R., et al., Federated Learning for Sustainable Energy Efficiency: A Privacy-Preserving Artificial
Intelligence Approach with Attention Mechanisms. 2025. p. 155–166.
78.Jradi, M., B.E. Madsen, and J.H. Kaiser, DanRETwin: A digital twin solution for optimal energy retrofit decisionmaking and decarbonization of the Danish building stock. Applied Sciences, 2023. 13(17): p. 9778.
79.Li, J. and S.X. Yang, Digital twins to embodied artificial intelligence: review and perspective. Intelligence &
Robotics, 2025. 5(1): p. 202–227.
80.Santos, G., et al. Multi-agent semantic interoperability in complex energy systems simulation and decision support.
in 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP). 2019. IEEE.
81.Vattano, S., Smart buildings for a sustainable development. Journal of Economics World, 2014. 2(6): p. 310–324.
82.Islam, F.S., A Multi-dimensional AI Framework for Sustainable Drinking Water Management: Integrating
Federated Learning, Digital Twins, and Blockchain. Journal of Engineering Research and Reports, 2025. 27(6): p. 466–
492.
83.Arun, M., et al., Investigating the performance of AI-driven smart building systems through advanced deep learning
model analysis. Energy Reports, 2025. 13: p. 5885–5899.
84.Han, J., et al., Generative Model-Based Building Evacuation Simulation for Safety Design. Journal of Building
Engineering, 2025: p. 114644.
85.Mohseni, S.-R., et al., FMI real-time co-simulation-based machine deep learning control of HVAC systems in smart
buildings: Digital-twins technology. Transactions of the Institute of Measurement and Control, 2023. 45(4): p. 661–
673.
86.Wang, R., et al., Personalized federated learning for buildings energy consumption forecasting. Energy and
Buildings, 2024. 323: p. 114762.
87.Radia, M.A., Next-generation monitoring: The fusion of IoT and AI, in Advanced Research Trends in Sustainable
Solutions, Data Analytics, and Security. 2025, IGI Global Scientific Publishing. p. 351–398.
88.Yang, C., et al., Big data driven edge-cloud collaboration architecture for cloud manufacturing: a software defined
perspective. IEEE access, 2020. 8: p. 45938–45950.
89.Szilagyi, I. and P. Wira. An intelligent system for smart buildings using machine learning and semantic technologies:
A hybrid data-knowledge approach. in 2018 IEEE Industrial Cyber-Physical Systems (ICPS). 2018. IEEE.
90.Katyara, S., et al., Benchmarking Sim2Real Gap: High-fidelity Digital Twinning of Agile Manufacturing. arXiv
preprint arXiv:2409.10784, 2024.
91.Yitmen, I., et al., AI-Driven Digital Twins for Enhancing Indoor Environmental Quality and Energy Efficiency in
Smart Building Systems. Buildings, 2025. 15(7): p. 1030.
92.Liu, Y., et al., Cognitive Digital Twin frameworks in manufacturing—A critical survey, evaluation criteria, and
future directions. Journal of Manufacturing Systems, 2025. 83: p. 597–611.
93.Rodić, A., Building AI-Supported Collaborative Awareness in Industrial Humanoids: Conceptual Framework and
Methodological Approach. ARTIFICIAL INTELLIGENCE IN INDUSTRY 4.0: THE FUTURE THAT COMES
TRUE: p. 62.
94.Zhou, L., et al., A heterogeneous streaming vehicle data access model for diverse IoT sensor monitoring network
management. IEEE Internet of Things Journal, 2024. 11(16): p. 26929–26943.
95.Nelufule, N., Federated Learning for Privacy-Preserving Energy Management in Distributed Power Systems. 2025.
58–66.
96.Wilk, P., N. Wang, and J. Li, Multi-Agent Reinforcement Learning for Smart Community Energy Management.
Energies, 2024. 17(20): p. 5211.
97.Frenette, J., Human-AI Collaboration Models: Frameworks for Effective Integration of Human Oversight and AI
Insights in Business Processes. International Journal for Research in Applied Science and Engineering Technology,
2024. 12.
98.Filippova, E., et al., Artificial Intelligence and Digital Twins for Bioclimatic Building Design: Innovations in
Sustainability and Efficiency. Energies, 2025. 18(19): p. 5230.
99.Yousef, L.A., H. Yousef, and L. Rocha-Meneses, Artificial intelligence for management of variable renewable
energy systems: a review of current status and future directions. Energies, 2023. 16(24): p. 8057.
100.Hofmeister, M., A Dynamic Knowledge Graph Approach to Creating Interoperability in Smart Cities Selected Case
Studies Towards Holistic Flood Impact Assessments and District Heating Operations. 2024, University of Cambridge
(United Kingdom).
101.Xu, H., et al., Leveraging generative ai for smart city digital twins: A survey on the autonomous generation of data,
scenarios, 3d city models, and urban designs. arXiv e-prints, 2024: p. arXiv: 2405.19464.
102.Akram, H., et al., Mangrove health: a review of functions, threats, and challenges associated with Mangrove Management Practices. Forests 2023; 14: 1698. doi. org/10.3390/f14091698, 2023.
103.Blokhin, D. and S. Demishonkova. Exact calculation of resonant frequency in a real parallel RLC circuit. in
ICECET'2025. 2025. IEEE.
104.Cacciuttolo, C., M. Navarrete, and E. Atencio, Renewable wind energy implementation in South America: A
comprehensive review and sustainable prospects. Sustainability, 2024. 16(14): p. 6082.