Optimizing Energy Use with TANGO RENOPS Scheduler

With electricity prices constantly fluctuating and carbon footprints on the rise, automated solutions for optimizing energy usage are becoming essential.
That is why, in TANGO, we implemented RENOPS – the Renewable Energy Forecast Production Service. This three-level tool can shift tasks both geographically and in time, based on predicted renewable energy availability and the lowest expected price or carbon emissions.

In this new video on TANGO’ KERs, Urban Kos, Anja Božič and Nejc Bat from XLAB dive deeper into the RENOPS Scheduler – a program that uses RENOPS forecasts to find the optimal time window for running energy-intensive tasks. It is ideal for recurring jobs such as AI model training, big data analytics, backups, and system scans. While it can be started manually, its real power lies in automating periodic tasks. Working alongside it is GeoShifter, which handles geographical optimization.

Both components come as a Python package designed for flexibility. Its smart CLI makes setup simple as it has flexible location settings, custom time control and works with any workload: from AI model training to backups and analytics.