Students of Novosibirsk State Technical University (NETI) are developing a system for analyzing the operating modes of the heating network using machine learning methods that will predict emergencies and technical accidents on heating networks.
"As part of the project, one of the industrial partners provided a set of data from sensors on one of the sections of the heating system. Based on these data, it was decided to build a pipeline of models: one would predict the next sensor indicator, the other would determine which type of event it might be suitable for. The system under development uses machine learning methods to analyze and optimize the operation of heating networks. By studying the interaction between individual heating systems and central heating stations, the system will predict and prevent damage to the infrastructure. For forecasting, an incoming data stream from sensors is needed: temperature, incoming and outgoing pressure, indicators for hot and cold water, pumps, etc. The list of data may vary," said Mark Starchenko and Ilya Zavgorodny, 4th—year students of the Faculty of Business at NSTU-NETI.
The value of the product lies in reducing repair costs and preventing about 50% more accidents. According to the developers, the system will also offer adaptive solutions in real time to maintain efficient network operation, taking into account environmental and other external factors. This approach is necessary to increase the reliability and efficiency of heating networks, reduce the risk of accidents and minimize economic losses.
At the moment, a prototype of the system has been created, which is planned to be finalized in the near future, after which a testing phase will be conducted. Students note that energy generating companies have already shown interest in the development.
The student team that developed this project took 1st place at the NSTU-NETI REACTOR acceleration program, which is being implemented as part of the federal project "Platform for University Technological Entrepreneurship" of the state program "Scientific and Technological Development of the Russian Federation".