From Optimization to Stability Preservation: A Self-Healing, Action-Bearing Digital Twin for Climate-Stressed Building Energy Systems

Seyed Reza Samaei; James Riffat

doi:10.65582/gti.2026.008

Authors

Seyed Reza Samaei World Society of Sustainable Energy Technologies, Nottingham, United Kingdom https://orcid.org/0000-0003-1627-1991
James Riffat World Society of Sustainable Energy Technologies, Nottingham, United Kingdom https://orcid.org/0000-0003-0374-4526

DOI:

https://doi.org/10.65582/gti.2026.008

Keywords:

Self-healing systems, Action-bearing digital twin, Building energy systems, Stability preservation, Deep non-stationary uncertainty, Structural physical adaptation, Climate-resilient control, Thermal comfort resilience

Abstract

Climate change exposes building energy systems to conditions beyond conventional control assumptions, where heatwaves, occupancy shifts, sensor drift, and degradation erode stability over time. This study proposes a self-healing digital twin that preserves system behavior within a defined stability envelope by integrating physics-based modeling, data assimilation, and adaptive response mechanisms. Self-healing is defined as preventing instability under non-stationary uncertainty. The framework is validated in closed-loop simulations across multiple stress scenarios, demonstrating robust stability under compound disturbances. Performance is benchmarked against rule-based control, classical model predictive control (MPC), and conventional digital twin supervision using a locked paired evaluation protocol with 30 matched realizations per scenario. Under compound uncertainty, the proposed framework reduces cumulative thermal comfort violation from 27.8 ± 3.6 (MPC) to 13.9 ± 1.9 , corresponding to a 50.0% reduction. The time outside the comfort band decreases from 11.2% to 4.6%. Drift accumulation, measured through residual energy, is reduced by 56.2%, while control smoothness improves by 39.5%. Total energy consumption is reduced by 3.38%, and mean recovery time following major disturbances decreases from 11.8 ± 1.8 h to 4.5 ± 0.9 h. Ablation and failure analyses show that stability preservation arises from the coordinated interaction of data assimilation, envelope-based stabilization, and structural adaptation. The results indicate that resilience-oriented stability management provides a more reliable operational paradigm than optimization-centric control for building energy systems under climate-driven uncertainty.

References

Arowoiya, V.A., Moehler, R.C. and Fang, Y., 2024. Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions. Energy and Built Environment, 5(5), pp.641–656. DOI: https://doi.org/10.1016/j.enbenv.2023.05.004.

Bamdad, K., Mohammadzadeh, N., Cholette, M. and Perera, S., 2023. Model predictive control for energy optimization of HVAC systems using EnergyPlus and ACO algorithm. Buildings, 13(12), 3084. DOI: https://doi.org/10.3390/buildings13123084.

Becerik-Gerber, B., Oradini, A.A. and Brilakis, M., 2022. Ten questions concerning human-building interaction research for improving the quality of life. Building and Environment, 226, 109681. DOI: https://doi.org/10.1016/j.buildenv.2022.109681.

Bortolini, R., Rodrigues, R., Alavi, H., Dalla Vecchia, L.F. and Forcada, N., 2022. Digital twins’ applications for building energy efficiency: A review. Energies, 15(19), 7002. DOI: https://doi.org/10.3390/en15197002.

Bundo, J., Selmanaj, D., Sharko, G., Svensson, S. and Zavalani, O., 2025. A simulation-based comparative study of advanced control strategies for residential air conditioning systems. Eng, 6(8), 170. DOI: https://doi.org/10.3390/eng6080170.

Buonanno, G., Stabile, L. and Morawska, L., 2020. Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment. Environment International, 141, 105794. DOI: https://doi.org/10.1016/j.envint.2020.105794.

Chand, S., Zheng, H. and Lu, Y., 2024. A vision-enabled fatigue-sensitive human digital twin towards human-centric human–robot collaboration. Journal of Manufacturing Systems, 77, pp.432–445. DOI: https://doi.org/10.1016/j.jmsy.2024.10.002.

Coupry, C., Noblecourt, S., Richard, P., Baudry, D. and Bigaud, D., 2021. BIM-based digital twin and XR devices to improve maintenance procedures in smart buildings: A literature review. Applied Sciences, 11(15), 6810. DOI: https://doi.org/10.3390/app11156810.

Drgoňa, J., Arroyo, J., Figueroa, I.C., Blum, D., Arendt, K., Kim, D., Perarnau Ollé, E., Oravec, J., Wetter, M., Vrabie, D.L. and Helsen, L., 2020. All you need to know about model predictive control for buildings. Annual Reviews in Control, 50, pp.190–232. DOI: https://doi.org/10.1016/j.arcontrol.2020.09.001.

Eneyew, D.D., Capretz, M.A.M. and Bitsuamlak, G.T., 2022. Toward smart-building digital twins: BIM and IoT data integration. IEEE Access, 10, pp.130487–130506. DOI: https://doi.org/10.1109/ACCESS.2022.3229370.

Eneyew, D.D., Capretz, M.A.M. and Bitsuamlak, G.T., 2024. Continuous model calibration framework for smart-building digital twin: A generative model-based approach. Applied Energy, 375, 124080. DOI: https://doi.org/10.1016/j.apenergy.2024.124080.

Guo, F., Ham, S.W., Kim, D. and Moon, H.J., 2025. Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building. Applied Energy, 377, 124467. DOI: https://doi.org/10.1016/j.apenergy.2024.124467.

Hasani, H. and Freddi, F., 2026. Condition-aware AI framework for automated structural health monitoring. Automation in Construction, 183, 106748. DOI: https://doi.org/10.1016/j.autcon.2025.106748.

Huang, S., Lutes, R., Faulkner, C.A., Vrabie, D.L., Katipamula, S. and Zuo, W., 2023. An open-source framework for simulation-based testing of building control strategies. Journal of Building Performance Simulation, 16(6), pp.631–643. DOI: https://doi.org/10.1080/19401493.2023.2191220.

Kaewunruen, S., Rungskunroch, P. and Welsh, J., 2019. A digital-twin evaluation of net zero energy building for existing buildings. Sustainability, 11(1), 159. DOI: https://doi.org/10.3390/su11010159.

Pandey, P.R. and Dong, B., 2023. Prediction of window opening behavior and its impact on HVAC energy consumption at a residential dormitory using deep neural network. Energy and Buildings, 296, 113355. DOI: https://doi.org/10.1016/j.enbuild.2023.113355.

Qian, Y., Leng, J., Zhou, K. and Liu, Y., 2024. How to measure and control indoor air quality based on intelligent digital twin platforms: A case study in China. Building and Environment, 253, 111349. DOI: https://doi.org/10.1016/j.buildenv.2024.111349.

Riffat, J., Samaei, S.R. and Pastakkaya, B., 2025. Advances, applications, and future directions of structural health monitoring in civil infrastructure: A comprehensive review. Energy Catalyst, 1, pp. 127–140. DOI: https://doi.org/10.61552/EC.2025.010.

Riffat, J., Nazari, K. and Samaei, S.R., 2026. AI-driven digital twin framework for adaptive and real-time structural health monitoring of offshore marine structures. Artificial Intelligence for Sustainable Cities, 1(1), pp. 90–104. DOI: https://doi.org/10.65582/aifsc.2026.006.

Riffat, J., Ahadpour Doudran, H. and Samaei, S.R., 2025. AI-driven digital twin for uncertainty-aware structural health monitoring of offshore wind turbines considering biofouling effects and reliability prediction. Green Technology and Innovation, 1(2), 025330013. DOI: https://doi.org/10.36922/GTI025330013.

Samaei, S.R. and Riffat, J., 2025. Intelligent structural health monitoring of jack-up platform legs using high-density sensor networks, real-time digital twins, and machine learning-based damage detection. Future Cities and Environment, 11. DOI: https://doi.org/10.70917/fce-2025-031.

Samaei, S.R. and Riffat, J., 2026. Cognitive digital twins for climate-resilient building energy systems: Diagnosis-informed control under extreme thermal stress. Artificial Intelligence for Sustainable Cities, 1(1), pp. 61–89. DOI: https://doi.org/10.65582/aifsc.2026.005.

Rodríguez, D., Urbieta, I.R., Velasco, Á., Campano-Laborda, M.Á. and Jiménez, E., 2022. Assessment of indoor air quality and risk of COVID-19 infection in Spanish secondary school and university classrooms. Building and Environment, 226, 109717. DOI: https://doi.org/10.1016/j.buildenv.2022.109717.

Sepasgozar, S.M.E., 2021. Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment. Buildings, 11(4), 151. DOI: https://doi.org/10.3390/buildings11040151.

Sood, D., Alhindawi, I., Ali, U., Finn, D., McGrath, J.A., Byrne, M.A. and O'Donnell, J., 2025. Machine learning-based multi-objective optimisation of energy consumption, thermal comfort and CO₂ concentration in energy-efficient naturally ventilated residential dwellings. Building and Environment, 267, 112255. DOI: https://doi.org/10.1016/j.buildenv.2024.112255.

Tabadkani, A., Roetzel, A., Li, H.X. and Tsangrassoulis, A., 2021. A review of occupant-centric control strategies for adaptive facades. Automation in Construction, 122, 103464. DOI: https://doi.org/10.1016/j.autcon.2020.103464.

Taheri, S., Hosseini, P. and Razban, A., 2022. Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review. Journal of Building Engineering, 60, 105067. DOI: https://doi.org/10.1016/j.jobe.2022.105067.

Tarragona, J., Gangolells, M. and Casals, M., 2024. Model predictive control for managing indoor air quality levels in buildings. Energy Reports, 12, pp.787–797. DOI: https://doi.org/10.1016/j.egyr.2024.06.053.

Walczyk, G. and Ożadowicz, A., 2024. Building information modeling and digital twins for functional and technical design of smart buildings with distributed IoT networks: Review and new challenges discussion. Future Internet, 16(7), 225. DOI: https://doi.org/10.3390/fi16070225.

Wang, X., Dong, B. and Zhang, J.J., 2023. Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season. Building Simulation, 16, pp.205–223. DOI: https://doi.org/10.1007/s12273-022-0936-6.

You, Y., Cai, B., Pham, D.T., Liu, Y. and Ji, Z., 2025. A human digital twin approach for fatigue-aware task planning in human–robot collaborative assembly. Computers & Industrial Engineering, 200, 110774. DOI: https://doi.org/10.1016/j.cie.2024.110774.