Bachelor- or Master-Thesis

AI-Driven Wind Energy Forecasting

13.03.2025, Diplomarbeiten, Bachelor- und Masterarbeiten

Wind energy is characterized by variability and non- linear behavior, complicating grid management and energy planning. Accurately forecasting wind energy generation is essential for optimizing grid operations, reducing energy costs, and ensuring system reliability. This thesis will explore innovative approaches to predict the generated wind energy over a 24-hour horizon by leveraging physical models (such as turbine power curves) and data-driven machine-learning techniques.

The increasing integration of renewable energy sources into modern power grids presents significant challenges and opportunities. Wind energy is characterized by variability and non- linear behavior, complicating grid management and energy planning. Accurately forecasting wind energy generation is essential for optimizing grid operations, reducing energy costs, and ensuring system reliability. This thesis will explore innovative approaches to predict the generated wind energy over a 24-hour horizon by leveraging physical models (such as turbine power curves) and data-driven machine-learning techniques.

Kontakt: lingga_aksara.putra@tum.de

More Information

https://www.epe.ed.tum.de/en/res/our-teaching-offer/our-bachelor-and-master-theses/