Renewable Energy Sources (RESs) are monitored by many high-quality sensors that produce vast amounts of high-frequency time series data. This can be used to increase the renewable energy production and longevity of the RESs, e.g., yaw misalignment detection and predictive maintenance for wind turbines. It is currently not possible for wind turbine manufacturers and owners to use this data due to limits on bandwidth and storage that are infeasible to increase. Thus, they store simple aggregates which remove valuable outliers and fluctuations. As a remedy, we demonstrate the new model-based Time Series Management System (TSMS) ModelarDB. The participants can experience how ModelarDB ingests time series on the edge and compresses them as segments with metadata and so-called models. The models represent values within a user-defined absolute or relative error bound (even 0 or 0%). Participants can adjust many parameters and see how the segments are transferred to the cloud using much less bandwidth and storage than other popular solutions like Apache Parquet and Apache TsFile, e.g., up to 90%–99% less than Apache Parquet. Participants can analyze the time series on the edge, in the cloud, and on the client using SQL or Python. On the client, ModelarDB runs in-process to integrate with, e.g., Python. Thus, participants can see how ModelarDB efficiently manages high-frequency time series across edge, cloud, and client.