Gépi tanulás, statisztikus adatfeldolgozás, jelfeldolgozáson alapuló alkalmazások
(pl. beszéd, kép, videó)
Mesterséges intelligencia és döntéstámogatás
This article presents a state-of-the-art review of machine learning (ML) methods and
applications used in smart grids to predict and optimise energy management.
The article discusses the challenges facing smart grids, and how ML can
help address them, using a new taxonomy to categorise ML models by method
and domain. It describes the different ML techniques used in smart grids as well as
examining various smart grid use cases, including demand response, energy forecasting,
fault detection, and grid optimisation, and explores how ML can improve these cases.
The article proposes a new taxonomy for categorising ML models and evaluates their
performance based on accuracy, interpretability, and computational efficiency. Finally,
it discusses some of the limitations and challenges of using ML in smart grid applications
and attempts to predict future trends. Overall, the article highlights how ML can
enable efficient and reliable smart grid systems.