2 papers
cs.NI2024
Demo: Testing AI-driven MAC Learning in Autonomic Networks
Leonard Paeleke, Navid Keshtiarast, Paul Seehofer +4
6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely requ…
math.OC2022
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
Eike Cramer, Leonard Paeleke, Alexander Mitsos +1
We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems. In particular, we use normalizing fl…