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.
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.
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.
