ENERGY EFFICIENCY AND RESOURCE SAVING IN CONSTRUCTION OF INDUSTRIAL FACILITIES: MODERN TRENDS AND PROSPECTS

Industrial construction remains a major driver of global energy consumption and resource depletion: the need to improve ‘energy efficiency’ in this sector goes beyond mere cost reduction, positioning itself as a fundamental element of sustainable economic growth and environmental stability – hence the need for a methodological paradigm shift in optimising energy use in industrial infrastructures. This research aims to develop an integrated framework for improving energy efficiency in industrial construction by synthesising advanced predictive modelling, ‘multi-parameter evaluation metrics’ and adaptive artificial intelligence-based energy management systems – thus creating a scalable and transferable methodology. A combination of empirical data analysis, computational modelling and statistical validation was used – key parameters such as ‘heat transfer coefficients’, ‘building envelope efficiency’ and ‘energy redistribution patterns’ were estimated in real-time industrial applications; regression models and correlation matrices were applied to check whether observed trends were consistent with theoretical benchmarks. The integration of ‘artificial intelligence-assisted energy distribution algorithms’ demonstrated unprecedented energy optimisation – heat loss reductions of up to 27% were achieved in high-intensity production areas, and overall energy efficiency improvements reached 19,6% in several case studies; a cost-benefit analysis confirmed an average payback period of 4,8 years, confirming the practicality of implementation at different industrial scales. The proposed methodological approach goes beyond direct industrial applications – its adaptability to related technology sectors, including ‘high-efficiency manufacturing’ and ‘large-scale infrastructure projects’, highlights its transdisciplinary potential; empirical validation of AI optimised energy saving strategies offers a replicable model for integration into national and international regulatory frameworks, ensuring compliance with stringent environmental standards while maintaining economics The study highlights the need for continuous improvement of algorithms – future work should aim to create ‘self-optimising systems’ capable of real-time reconfiguration in response to fluctuations in energy demand, bridging the gap between theoretical efficiency modelling and evolving industrial conditions.