MACHINE LEARNING IN POWER INDUSTRY

Ural Federal University

The course contains the description of the best practices of AI & ML integration in up-to-date fuel and energy sectors. The major part of the course is industry-oriented practical training, which provides necessary knowledge and skills in the sphere of data analysis and machine learning to manage real story problems of the industry.

The main goal of the course is to provide a comprehensive understanding of the theory and practice of intelligent systems and the application of machine learning methods in the sphere of power industry (in Smart Energy Systems).

Within the framework of the discipline students will carry out their own theoretical and experimental research in the field of artificial intelligence, machine learning, design of Smart Energy Systems and master the skills of data representation, processing and analysis.

The target audience of the course covers bachelor and master students, pursuing the degree in Computer Science, Power Engineering and Electronics.

Project goals:

  • Acquiring of new knowledge, practical experience along with mastering the forefront technologies in power industry and mathematics and software engineering;
  • Establishing business contacts between the students and industrial partners – leading energy and fuel companies of Russia;
  • Getting experience in multi-project work disciplinary;
  • Dealing with real-story challenges, that high-technological energy and fuel companies are currently facing;

Publishing the project results in peer-reviewed scientific transactions.

The program of the internship is fully based on the results of Erasmus+ KA2 ESSENCE CBHE ESSENCE project realization at Ural Federal University. Been developed in collaboration with the EU experts and industrial companies, the program of the internship presupposes step-by-step mastering of up-to-date mathematical approaches pushing learners towards the solution of the real industrial case. The internship may be referred to as “essence” of the ESSENCE project, since it incorporates the main of the “Artificial intelligence and machine learning in Smart Energy systems” discipline, active teaching technologies, favors digital competences and soft skills development, supports distant and blended learning modes.

More information about the program and admission procedure could be found here.