Machine Learning Applications in Energy Systems

Today we introduce one of the first courses on our programme: Machine Learning in Energy. It covers the main machine-learning methods used to address energy engineering problems and the specifics of their application.

Main topics:

– Supervised learning for classification and regression.

– Unsupervised learning for clustering.

– Bio-inspired algorithms and reinforcement learning for optimisation.

The course also covers data preprocessing and analysis. Methods include linear models, support vector machines, decision trees, Bayesian methods, nearest-neighbour methods, neural networks, ensemble algorithms and fuzzy logic.

One of the lecturers is Alexandra Ilmarovna Khalyasmaa, Associate Professor and head of the Power Engineering Digital Twins Laboratory, PhD in Engineering. She has 14 years of professional experience.

Research interests:

– Diagnostics and life-cycle forecasting for electric power equipment

– Machine learning and artificial intelligence

– Distributed generation and renewable energy sources

Lecturer's publications:

science.urfu.ru

scopus.com

publons.com

elibrary.ru