2025, Vol. 12, No. s4. - go to content...

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DOI: 10.15862/12FAOR425 (https://doi.org/10.15862/12FAOR425)

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Naumov I.S., Goncharov A.V. Modern trends and features of climate control system automation for building complexes with decentralized heating. Russian journal of resources, conservation and recycling. 2025; 12(s4). Available at: https://resources.today/PDF/12FAOR425.pdf (in Russian). DOI: 10.15862/12FAOR425

Modern trends and features of climate control system automation for building complexes with decentralized heating

Naumov Ivan Sergeevich
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: isnaumov@yandex.ru

Goncharov Andrey Vitalievich
K.G. Razumovsky Moscow State University of Technologies and Management (the First Cossack University),
Moscow, Russia
E-mail: a.goncharov@mgutm.ru

Abstract. The modern paradigm of climate control in building complexes is characterized by a fundamental transformation from centralized heating systems to decentralized architectures, integrating Internet of Things technologies, artificial intelligence, and digital twins to achieve optimal energy efficiency and user comfort. The study is devoted to a comprehensive analysis of modern trends and features of climate control system automation under decentralized heating conditions through the prism of technological innovations, regulatory requirements, and economic efficiency. The subject of the research includes architectural solutions, control algorithms, and technological platforms for implementing intelligent climate control systems in buildings of various purposes. The concepts of distributed control based on tokenized scheduling algorithms, machine learning methods for thermal load prediction, and approaches to integrating BIM models with real-time systems are considered. Analysis of the evolution of automation systems demonstrates a transition from reactive deviation control to proactive strategies based on predictive analytics, with the implementation of intelligent algorithms providing a 20–35 % reduction in energy consumption while simultaneously improving user comfort. Key technological transformation drivers have been identified: development of wireless sensor networks based on LoRaWAN and NB-IoT protocols, implementation of cloud analytics platforms with big data processing capabilities, and application of neural network models for adaptive control. The developed conceptual model of multi-level automation architecture integrates the physical level of sensors and actuators, network data transmission level, processing and analytics level based on machine learning, and visualization and human-machine interface level. The scientific novelty of the research lies in systematizing modern approaches to decentralized climate control, identifying the specifics of the Russian context for implementing intelligent systems, and developing a comprehensive model that takes into account the relationship between technological, economic, and regulatory factors. Practical significance is determined by the possibility of applying the developed recommendations in designing automation systems for new and reconstructed buildings, optimizing existing climate control systems, and forming strategies for digital transformation of engineering infrastructure.

Keywords: climate automation; decentralized heating; Internet of Things; machine learning; digital twins; energy efficiency; BIM technologies; predictive control; smart HVAC; distributed control systems

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