THE ROLE OF ARTIFICIAL INTELLIGENCE IN ADAPTIVE ARCHITECTURE: MODELING, ANALYSIS, AND OPTIMIZATION OF SMART BUILDING PARAMETERS
DOI:
https://doi.org/10.31650/2786-6696-2026-15-24-33Keywords:
adaptive architecture, artificial intelligence, smart building, machine learning, parameter optimization, experimental–statistical modelAbstract
This study investigates the integration of artificial intelligence (AI) algorithms into adaptive architecture systems for the purpose of modeling, analyzing, and optimizing smart building parameters. Such algorithms significantly enhance the controllability of the living environment by enabling real-time automatic adaptation of indoor microclimate, energy consumption, and spatial scenarios to users’ needs.
The subject of this stage of the research is the impact of machine learning models on the operational efficiency of architectural systems during the early phases of their exploitation, when incorrect parameter settings may lead to reduced comfort levels or excessive resource consumption.
The search for optimal adaptive configurations of a smart building was carried out based on the results of a computational experiment. Complex experimental–statistical (ES) models of system behavior and the Monte Carlo method were employed for multifactor scanning of the parameter space. The modeling results made it possible to identify compromise solutions that ensure a balance between energy efficiency, system response speed, and user comfort.
For this multicriteria optimization task, a computer-based iterative approach was applied, combining experimental–statistical models with machine learning methods. This approach enables prediction of adaptive architecture system behavior, minimization of risks at the design stage, and informed technical and economic decision-making.
Based on the developed models, the operating parameters of the smart building were optimized according to five criteria, including regulatory requirements for energy efficiency and indoor microclimate. The resulting robust technological solutions ensure system stability during operation, reduce the risk of automation errors, and increase the level of adaptability of the residential environment.
Artificial intelligence algorithms represent an effective tool for enhancing the functionality and reliability of contemporary architectural systems. Despite the increased computational resources required for their implementation, the use of AI contributes to energy optimization, improved spatial adaptability, and the development of intelligent human-building interaction scenarios.
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