Price: 0
Number of applications: 5
07.01.26 (inclusive)
By agreement
MVP
ICT tasks
Electric power industry
Information processing and transformation
Software/ IS
Energy-intensive industrial complexes face a number of technological and managerial constraints: there is no automated system for collecting minute data from production equipment; High volumes of unstructured data lead to errors and distortions, and there is no normalization or filtering of anomalies.; calculations of energy efficiency and operating modes are performed manually, which reduces accuracy and efficiency.; There are no mathematical models that can predict energy consumption and optimize equipment parameters.
Expected effect: improving the energy efficiency of production processes by optimizing control parameters; reduced energy consumption through recommendations based on machine learning models; improving the accuracy of energy resource planning by predicting consumption; acceleration of decision-making, thanks to operational analytics and visualization of parameters; minimizing errors and anomalies through automated data normalization; creating the basis for further digitalization of production processes. It is expected that the implementation of the system will lead to a significant reduction in energy consumption, improve the technological stability of processes and increase the efficiency of production facilities.
Dmitry Timofeev
Purpose and description of task (project)
The aim of the project is to create a software system that improves the energy efficiency of production processes through automated data collection, calculations of key parameters, forecasting energy consumption and issuing recommendations on optimal operating modes of equipment. The system under development should include modules for data integration, machine learning, analysis, and visualization. The system provides calculation of actual energy efficiency indicators, modeling of energy consumption in various modes, automatic generation of recommendations, real-time parameter monitoring and provision of analytical information to the user in a convenient form. The project provides for the implementation of an architecture that includes subsystems for data collection and normalization, forecasting, optimization, calculations, visualization, analytics, reporting and administration. The system should support continuous retraining of models to improve the accuracy of forecasts and the effectiveness of recommendations.
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