20:00, 08 December 2025

Scientists are developing a 'smart' system to prevent accidents in Siberia's heating networks

Scientists in Russia are developing an intelligent monitoring and prediction system for Siberia’s heating networks, aimed at preventing accidents, saving resources, and increasing reliability

From Raw Data to Predictive Intelligence

Researchers at Novosibirsk State Technical University are creating a unique software system designed to monitor and forecast the condition of heating networks. The technology is expected to prevent accidents, optimize resource use, and significantly improve the stability of heat supply across Siberia.

The foundation of the system is a five‑year dataset collected from heat distribution units in schools and kindergartens. Engineers are using this information to build “reference profiles” that represent the normal operating state of heating systems. Any deviation from these profiles will act as an early warning sign of potential malfunction.

“As of 2020, we have accumulated a large dataset and are now working on generalizing it to obtain a typical operational profile. This ‘reference’ will allow us to compare objects, monitor how they function, and detect anomalies,” explained project lead and associate professor Alexander Dvortsevoy.

Neural Networks Take Over the Heavy Lifting

The data will be processed by neural networks. The uniqueness of the project lies in its evolution from simple data collection to intelligent analysis. The next stage involves developing a machine‑learning model capable of identifying hidden patterns, predicting risks, and recommending solutions. This marks a major shift from digital record‑keeping to full predictive infrastructure management.

“The Siberian Generating Company has an enormous dataset — hundreds of thousands of parameters. It would be extremely difficult for a person or even a team to analyze this kind of volume. A machine must do this work,” Dvortsevoy noted.

A Strategic Breakthrough for Utilities

The project carries strategic importance not only for Russia, but for global utilities as well. Heating systems remain critical infrastructure across cold‑climate regions, and the ability to prevent failures through predictive analytics could fundamentally change maintenance strategies worldwide.

The software prototype is scheduled for presentation in spring 2026. The development team includes energy‑engineering and computer‑science students, ensuring a strong practical focus. Major Siberian energy companies and municipal management organizations have already expressed interest, highlighting the high demand for such technologies.

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