Machine Learning for Power Engineering Applications

This course introduces the main machine-learning methods used in energy applications and their specific implementation requirements.

Core topics include:

– supervised learning for classification and regression;

– unsupervised learning for clustering;

– bio-inspired algorithms and reinforcement learning for optimisation.

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