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APPLICATION OF THE “BUILDING ENERGY AUDIT” PROGRAM FOR MODELING THE ENERGY CONSUMPTION OF RESIDENTIAL BUILDINGS

Muzaffar Khudayarov Muzaffar K. Sarvar Kurbonov Sarvar K.

Abstract

The article examines the modeling and balancing of residential building energy consumption, taking into account various influencing factors. To solve this problem, a number of methods and software tools are used, based on different approaches and requiring varying amounts of input data regarding the building and external climatic conditions. As an example, the energy modeling process of a residential building using the "BUILDING ENERGY AUDIT" software which is based on physical laws and accounts for quasi-stationary states over a one-month period is presented. The simulation results demonstrated that after implementing energy efficiency measures in the residential building's heating and domestic hot water systems, it is possible to reduce energy consumption to 219.05 kWh/m² or by approximately 15 % relative to the current level and to upgrade the energy efficiency class from the current level D (normal) to level C (increased). The modeling results obtained using this software can serve as input data for building energy certification, energy audits, and assessing compliance with building codes and standards.

Keywords

quasi-dynamic modeling residential building building energy audit energy consumption balance energy efficiency energy-saving measures
Authors
Muzaffar Khudayarov
Tashkent State Technical University
ORCID: 0000-0003-2829-8980
Sarvar Kurbonov
JV LLC “SKB”
ORCID: 0000-0002-7651-1048

How To Cite

Journal Style
Muzaffar, K.; Sarvar, K. APPLICATION OF THE “BUILDING ENERGY AUDIT” PROGRAM FOR MODELING THE ENERGY CONSUMPTION OF RESIDENTIAL BUILDINGS. Innovatsion texnologiyalar, 2026, 63(3), 103-114. https://doi.org/10.70769/218-4732.ITJ.2026-3.11
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References

  1. Yu J., Chang W.-S., Dong Y. Building Energy Prediction Models and Related Uncertainties: A Review // Buildings. – 2022. – Vol. 12, No. 8. – Article 1284. – DOI: 10.3390/buildings12081284
  2. Lam K. P., Zhao J., Ydstie E., Wirick J., Qi M., Park J. An EnergyPlus Whole Building Energy Model Calibration Method for Office Buildings Using Occupant Behavior Data Mining and Empirical Data // Proceedings of ASHRAE/IBPSA-USA Building Simulation Conference. – 2014. – P. 160 – 167.
  3. Hou D., Zhan D., Wang L., Hassan I. G., Sezer N. Development and performance assessment of a new open-source Bayesian inference R platform for building energy model calibration // Discover Mechanical Engineering. – 2023. – Vol. 2. – Article 19. – DOI: 10.1007/s44245-023- 00027-2.
  4. EnergyPlus ™ — программа для моделирования энергопотребления всего здания. https://energyplus. net /.
  5. Графическая программная среда, для моделирования зданий (включая энергетическое моделирование по стандарту LEED). https://www.trnsys.com/.
  6. Пакеты для оценки энергопотребления DesignBuilder. https://designbuilder.co.uk/.
  7. Building Energy ST Formerly hosted by US Dept. of Energy. https://www.buildingenergysoftwaretools.com/. ОП Н П Ж П INNOVATSION TEXNOLOGIYALAR INNOVATIVE TECHNOLOGIES ИННОВАЦИОННЫЕ ТЕХНОЛОГИИ 2026 - yil 3 (63) - son 2026 volume 63, number 3 Том 63 No 3, 202 6 ISSN 2181 - 4732 E - ISSN 2181 - 4 015 114
  8. IDA ICE (Indoor Climate and Energy) инструмент для моделирования зданий и его систем. https://w ww. equa. se / en / ida - ice / case - studies.
  9. Amasyali K., El-Gohary N. M. A review of data-driven building energy consumption prediction studies // Renewable and Sustainable Energy Reviews. – 2018. – Vol. 81. – P. 1192 – 1205. – DOI: 10.1016/j.rser.2017.04.095.
  10. Yesilyurt H., Dokuz Y., Dokuz A. S. Data-driven energy consumption prediction of a university office building using machine learning algorithms // Energy. – 2024. – Vol. 310. – Article 133242. – DOI: 10.1016/j.energy.2024.133242.
  11. Wang S., Xu X. Simplified building model for transient thermal performance estimation using GA-based parameter identification // International Journal of Thermal Sciences. – 2006. – Vol. 45, No. 4. – P. 419 – 432. – DOI: 10.1016/j.ijthermalsci.2005.06.009.
  12. Von Krannichfeldt L., Orehounig K., Fink O. Combining physics-based and data-driven modeling for building energy systems // Applied Energy. – 2025. – Vol. 391. – Article 125853. – DOI: 10.1016/j.apenergy.2025.125853.
  13. Michalakopoulos V., Pelekis S., Kormpakis G., Karakolis V., Mouzakitis S., Askounis D. Data-driven building energy efficiency prediction using physics-informed neural networks // 2024 IEEE Conference on Technologies for Sustainability (SusTech). – 2024. – P. 84 – 91. – DOI: 10.1109/SusTech60925.2024.10553513.
  14. Somu N., Gauthama Raman M. R., Ramamritham K. A deep learning framework for building energy consumption forecast // Renewable and Sustainable Energy Reviews. – 2021. – Vol. 137. – Article 110591. – DOI: 10.1016/j.rser.2020.110591.
  15. Li, Y.; O’Neill, Z.; Zhang, L.; Chen, J.; Im, P.; DeGraw, J. Grey-box modeling and application for building energy simulations — A critical review.Renew.Sustain.Energy Rev. 2021, 146, 111174.
  16. Lecocq E. M., Pascual J., Salom J. Development of physically coherent grey-box models for residential buildings using a simplified adjustment method // Energy and Buildings. – 2025. – Vol. 328. – Article 115215. – DOI: 10.1016/j.enbuild.2024.115215.
  17. КМК 2.01.04 - 18 «Строительная теплотехника», Минстрой РУз, Ташкент, 2018 г., https://lex.uz/docs/6874131.
  18. КМК 2.01.18 - 24 «Нормативы расхода энергии на отопление, вентиляцию и кондиционирование зданий и сооружений», Минстрой РУз, Ташкент, 2024 г., https://lex.uz/ru/docs/7340715.
  19. КМК 2.01.01 – 22 «Климатические и физико - геологические данные для проектирования», Минстрой РУз, Ташкент, 2022 г., https://lex.uz/docs/6363141.