Development and verification of a computational software-based method for modeling the indoor thermal regime under emergency heating shutdowns

Objective. This study presents the development and verification of a computational method for predicting indoor air temperature during emergency heating shutdowns. The aim of the study is to verify the correctness of a digital tool designed to calculate the indoor temperature regime, the rate of air temperature drop and heat losses under intermittent energy supply conditions, taking into account the heat-storage properties of enclosing structures and heating system components.
Method. The methodological basis is the theory of thermal stability and the law of regular thermal conditions of the first kind, which describes the cooling process of heating system elements and enclosing structures. The software algorithm consists of two blocks that process the geometric, thermophysical, and operational parameters of the room, external enclosures, and heating system. The software is designed for research and engineering calculations.
Result. The developed tool was tested to determine indoor air temperature in both dimensional and nondimensional forms, the rate of temperature drop and heat losses during the room cooling period. Testing confirmed the stability of calculations and their consistency with the physical heat transfer model.
Conclusion. The novelty lies in the algorithmic integration of air cooling processes, building envelopes, and the heating system within a single software and computing approach. The method automates calculations of room temperature conditions during heating system outages and is designed to analyze the reliability, energy efficiency, and operational sustainability of buildings.

1. Malyavina, E.G., Suslova, V.V. The effect of the enclosing structures heat capacity of the room on its temperature after the heating is turned off // News of Higher Educational Institutions. Construction. 2025; 2:76-84. https://doi.org/10.32683/0536-1052-2025-794-2-76-84. (In Russ)

2. Karpov, D.F., Pavlov, M.V., Abramova, E.V., Vafaeva, Kh.M. Investigation of the indoor air temperature in a civilian building after an emergency shutdown of the heating system. Herald of Daghestan State Technical University. Technical Sciences. 2024;51(2):197-207. DOi.org/10.21822/2073-6185-2024-51-2-197-207. (In Russ)

3. Shumilin, E.V., Ilyin, N.V. Consequences of disconnecting the heating system of a separate apartment in an apartment building. Far East: Problems of Architectural and Construction Complex Development. 2023; 1: 331-334. (In Russ)

4. Pavlov, M.V., Karpov, D.F., Khraponova, L.V. Air temperature prediction in a cultivation facility after heating system shutdown. Prirodoobustrojstvo. 2025;3:30-37. DOI.org/10.26897/1997-6011-2025-3-30-37. (In Russ)

5. Malyavina, E.G., Akhverdashvili, R.G. Determination of the room cooling time after switching off the heating system, taking into account the thermal stability of indoor and outdoor multilayer enclosing structures. News of Higher Educational Institutions. Construction. 2023;10:83-96 https://doi.org/10.32683/0536-1052-2023-778-10-83-96. (In Russ)

6. Razakov, M.A. Engine room thermal density specials in heating systems shutdown process. Power engineering: research, equipment, technology. 2022; 24.(6):133-142. DOI.org/10.30724/1998-9903-2022-24-6-133-142. (In Russ)

7. Borovkov, A.I., Vafaeva, Kh.M., Vatin, N.I., Ponyaeva, I. Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review. Construction Materials and Products. 2024;7(4). https://doi.org/10.58224/2618-7183-2024-7-4-7

8. Feng, Yuxin and Hwang, Bon‐Gang. Toward a Sustainable Paradigm: Navigating Digital Transformation in the Construction Industry. Sustainable Development. 2025;33. DOI.org/9335-9351. 10.1002/sd.70162

9. Lima, Maria and Cunha, Thaís and Cândido, Luis and de Paula Barros Neto, José. Digital Transformation in the Construction Industry: Lessons and Challenges from the Journey of Brazilian Construction Companies. Sustainability. 2025; 18:407. https://doi.org/10.3390/su18010407

10. Soltani, Sahar and Giusti Gestri, Lisa and Pavez, Fernando and Maxwell, Duncan. A multi-staged analysis to propose a construction hub framework to drive sustainable and digital transformation in the construction industry. Smart and Sustainable Built Environment. 2025; 1. https://doi.org/10.1108/SASBE-11-2024-0458

11. Strizhak, P.A., Morozov, M.N. Thermal regime of a building under emergency operation of the heat supply system. Chief Power Engineer. 2014;7:39-45. (In Russ)

12. Chen, Lu and Han, Zhipan and Wu, Yujie and Zhang, Zhongshan and Liu, Yu and Li, Xiaomeng and Cao, Hui and Chen, Yongxu and Yang, Kun. Research on Energy-Efficient Retrofit Design and Thermal Load Characteristics of Public Buildings Based on Optimal Thermal Comfort. Buildings. 2025;15:4066. https://doi.org/10.3390/buildings15224066

13. Bianco, Nicola and Fragnito, Andrea and Mauro, Gerardo and Menna, Costantino and Roselli, Carlo. Thermal analysis of 3D-printed panels for enhanced building envelope performance // Journal of Physics: Conference Series. 2025; 3143:012048. https://doi.org/10.1088/1742-6596/3143/1/012048

14. Ponni, M and Hemalatha, A and Baskar, S and Kuchana, Rakesh. AI-Based Optimization Of HVAC Systems For Smart Energy-Efficient Buildings. International Journal of Environmental Sciences. 2025; 11: 950. https://doi.org/10.64252/cgw1g472

15. Piras, Giuseppe and Muzi, Francesco and Ziran, Zahra. A Data-Driven Model for the Energy and Economic Assessment of Building Renovations. Applied Sciences. 2025;15:8117. https://doi.org/10.3390/app15148117

16. Ali, Dalia and Motuzienė, Violeta. Applying AI to HVAC Systems: a Critical Analysis of Data-Related Challenges // CONECT. International Scientific Conference of Environmental and Climate Technologies. 2025;18. https://doi.org/10.7250/CONECT.2025.002

17. Romaios, A. and Paravantis, John and Giannadakis, A. and Malefaki, Sonia and Souliotis, Manolis and Antzoulatou, A. and Georgiou, Paraskevas and Nikolakopoulos, Pantelis and Mihalakakou, G. Simulation‐Based and Machine Learning Methodologies for Energy Optimization in Buildings. Wiley Interdisciplinary Reviews: Energy and Environment. 2026; 15. https://doi.org/10.1002/wene.70021

18. Vafaeva, Kh.M., Gaevskaya, Z.A. Optimization algorithms and their application in information modeling of cultural heritage objects. Innovation and Investment. 2021;12:175-179. https://doi.org/10.13140/RG.2.2.20715.92965. (In Russ)

19. Zaripov, A.A., Guzanov, R.O., Nuriev, M.G. et al. Development and testing of an automated detection and control system for engineering devices based on predictive analytics. International Research Journal. 2025; 10(160). https://doi.org/10.60797/IRJ.2025.160.72. (In Russ)

20. Darwish, Hiba and Khapper, Krupa and Graves, Corey and Gokaraju, Balakrishna and Tesiero,Raymond. Intelligent Energy Optimization in Buildings Using Deep Learning and Real-Time Monitoring. Energies. 2026; 19. https://doi.org/10.3390/en19020379

21. Samarin, O.D. The calculation of building cooling under emergency conditions to ensure their heating reliability.Vestnik MGSU. 2019;14(4):496-501. DOI.org/10.22227/1997-0935.2019.4.496-501. (In Russ)

22. Rafalskaya, T.A. Effects of moisture accumulation in outer walls on thermal mode of premises in case of emergency heat supply.Herald of Daghestan State Technical University. Technical Sciences. 2021;48(2): 113-123. https://doi.org/10.22227/10.21822/2073-6185-2021-48-2-113-123. (In Russ)

23. Maracchini, Gianluca and D'orazio, Marco. Indoor Environment in Emergency Architectures // SpringerBriefs in Applied Sciences and Technology. 2025. https://doi.org/10.1007/978-3-032-06795-1_3

24. Zhang, Jiarui and Mu, Yunfei and Wu, Zeqing and Liu, Zhe and Gao, Yi and Li, Hairun. Optimal scheduling method of regenerative electric heating for emergency residential building heating: An affine arithmeticbased model predictive control approach. IET Energy Systems Integration. 2022;5. https://doi.org/10.1049/esi2.12082

25. Balasanian, Hennadii and Klymchuk, Oleksandr and Luzhanska, Ganna and Aksyonova, Inna and Voronenko, Serhii. Influence of the Building’s Thermal Insulation on Intermittent Heating Mode Efficiency. Management Systems in Production Engineering. 2024;32:484-488. https://doi.org/10.2478/mspe-2024-0045

26. Deshko, Valeriy and Bilous, Inna and Buyak, Nadia and Hetmanchuk, Hanna and Hurieiev, Maksym and Odineca, Tatjana. Parametric Analysis of Heat Flows Through Building Envelope Considering Orientation, Massiveness, and Intermittent Heating Modes. Rocznik Ochrona Środowiska. 2025;27:110-120. https://doi.org/10.54740/ros.2025.010

27. Streckiene, Giedre and Bielskus, Juozas and Rogoža, Artur and Lapinskiene, Vilune and Miseviciute, Violeta. Evaluating the integration of apartment building heating systems with low-temperature district heating networks. Mokslas - Lietuvos ateitis. 2025; 17:1-8. https://doi.org/10.3846/mla.2025.23939

28. Taradai, O and Bugai, Volodymyr and Gvozdetskii, O and Burda, Y and Diachenko, S. Improving the operational reliability of centralized heat supply systems. IOP Conference Series: Earth and Environmental Science. 2025. 1499: 012054. https://doi.org/10.1088/1755-1315/1499/1/012054

29. Karpov, D.F., Pavlov, M.V., Sinitsyn, A.A. Integrated energy-saving diagnostics of the technical condition of fencing structures of capital construction objects and engineering systems based on thermal control. Energy saving and Water treatment. 2020; 2 (124):29-33. (In Russ)

30. Frolova, A.A., Nuriev, M.G., Shkinderov, M.S. et al. Certificate of State Registration of a Computer Program No. 2025619659. Software for determining the indoor air temperature in a room after the heating system shutdown: No. 2025617254: declared 28.03.2025: publ. 16.04.2025;4. Right holder of National Research Moscow State University of Civil Engineering. https://doi.org/10.13140/RG.2.2.28674.80326. (In Russ)