Students Apply Machine Learning to Solar Generation Forecasts

In late May, as part of cooperation with the Ural Power Engineering Institute, pupils from Gymnasium No. 13 in Yekaterinburg presented projects using machine learning to forecast electricity generation at a solar power plant.

Throughout the school year, researchers from the Power Engineering Digital Twins Research Laboratory held classes and mentored the students. Together with their supervisors, they discussed the fundamentals of energy and renewables, defined the research problem, worked with data and conducted engineering investigations.

The projects were also evaluated by EL5-Energo, illustrating how machine-learning approaches and sound data practices can be relevant to industry.

The project presentations included a separate lecture on sustainable development and why these challenges matter to cities and the energy sector.