Information and software modules for decision support in achieving design energy efficiency of buildings
DOI:
https://doi.org/10.32347/2707-501x.2026.57(1).257-269Keywords:
design energy efficiency, information and software module, decision support, thermal balance, multifactor modeling, multi-criteria optimization, NPV, digital designAbstract
Information and software decision-support modules for achieving design energy efficiency of buildings are considered as integrated digital systems that combine computational algorithms, multifactor modeling, and optimization procedures within a unified analytical environment. Design energy efficiency is interpreted as an integral characteristic of a building, formed at the design stage and determined by the thermal performance parameters of envelope structures, engineering system characteristics, and regional climatic conditions.
The core of the information and software module is a formalized thermal balance model that accounts for heat losses through envelope structures, infiltration losses, and solar heat gains. To improve forecasting accuracy, a multifactor regression model of energy consumption is applied, enabling the identification of priority parameters influencing overall performance. The structure of the software complex includes a data input block, computational module, scenario analysis module, optimization module, and analytical module, integrated through an iterative information loop.
The optimization module implements a multi-criteria approach that simultaneously considers annual energy consumption, investment costs, and net present value. The formation of a set of Pareto-efficient alternatives ensures the rational selection of design solutions based on both energy performance and economic feasibility. Practical implementation of the module confirmed the possibility of reducing specific energy consumption by up to 41% while maintaining an optimal balance between capital expenditures and economic return.
The integration of algorithmic models, scenario analysis, and economic evaluation forms a digital tool for managing design energy efficiency in real time. The results demonstrate the feasibility of transitioning from isolated thermal calculations to comprehensive decision-support systems that ensure adaptability and enhanced justification of design solutions. The proposed approach creates prerequisites for developing intelligent energy-efficient projects focused on long-term operational stability and compliance with modern sustainable development standards.
References
Attia S., Hensen J.L.M., Beltrán L., De Herde A. Selection criteria for building performance simulation tools: contrasting architects’ and engineers’ needs. Journal of Building Performance Simulation, 2012, 5(3), 155–169. https://doi.org/10.1080/19401493.2010.549573.
Ma Z., Cooper P., Daly D., Ledo L. Existing building retrofits: Methodology and state-of-the-art. Energy and Buildings. 2012. Vol. 55. P. 889–902. https://doi.org/10.1016/j.enbuild.2012.08.018.
Wang L., Gwilliam J., Jones P. Case study of zero energy house design in UK. Energy and Buildings. 2009. Vol. 41(11). P. 1215–1222. https://doi.org/10.1016/j.enbuild.2009.07.001.
Hensen J.L.M., Lamberts R. Building Performance Simulation for Design and Operation. London: Spon Press, 2011. 536 р.
Li C., Chen Yo. Modeling and optimization method for building energy performance in the design stage. Journal of Building Engineering, 2024, Vol. 87, 109019. https://doi.org/10.1016/j.jobe.2024.109019.
Czerwoniec A., Torzewicz T., Samson A., Janicki M. Estimation of heat transfer coefficient temperature dependence from cooling curve measurements. 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES), Torun, Poland, 2015, pp. 422-425. DOI: 10.1109/MIXDES.2015.7208555.
Abu Saleh Md., Rasel H.M., Ray B. A comprehensive review towards resilient rainfall forecasting models using artificial intelligence techniques. Green Technologies and Sustainability, 2024, Vol. 2, Issue 3, 100104. https://doi.org/10.1016/j.grets.2024.100104.
Wang L., Zmeureanu R., Rivard H. Applying multi-objective genetic algorithms in green building design optimization. Building and Environment. 2005. Vol. 40(11). P. 1512–1525. https://doi.org/10.1016/j.buildenv.2004.11.017.
Chernyshev D., Ryzhakova G., Honcharenko T., Petrenko H., Chupryna I., Reznik N. Digital Administration of the Project Based on the Concept of Smart Construction. In: Alareeni, B., Hamdan, A. (eds) Explore Business, Technology Opportunities and Challenges After the Covid-19 Pandemic. ICBT 2022. Lecture Notes in Networks and Systems, 2023, Vol. 495. Springer, Cham. https://doi.org/10.1007/978-3-031-08954-1_114.
Nguyen A.-T., Reiter S., Rigo P. A review on simulation-based optimization methods applied to building performance analysis. Applied Energy. 2014. Vol. 113. P. 1043–1058. https://doi.org/10.1016/j.apenergy.2013.08.061.
Рижакова Г., Приходько Д., Поколенко В., Петруха Н., Чуприна Ю., Хоменко О. Оновлення науково-методичних підходів до побудови полікритеріальної системи адміністрування діяльністю підприємств-стейкхолдерів проєктів будівництва. Просторовий розвиток, 2022, 1, 218–233. https://doi.org/10.32347/2786-7269.2022.1.218-233.
