COMET - Collaborative Machine Learning for the Energy Transition

Future energy systems will produce vast amounts of data, which is owned by many different parties and very distributed. Effectively using this data for both machine learning based forecasting (e.g. renewable energy generation) and control related tasks (e.g. operating distribution grids or household heating) is critical for managing the renewable energy transition. Variable electricity generation, electrification of energy usage and sector coupling all benefit from accurate forecasting and control methods. COMET researches advanced intelligent methods to achieve these goals at the intersection of energy management and data markets. 

COMET is part of JADS (Joint Academy of Doctoral Studies), the flagship partnership of TUM and Imperial College London. It is part of cohort 2022/2023: "Circular Economy and Transition to Zero Pollution".