Building OpenSTEF 4.0 Alpha
AW1.126 | Day 1 | 15:30 - 15:55 | Speakers: Bart Pleiter, Egor Dmitriev
Abstract
The electricity grid faces increasing complexity as solar panels, wind turbines, EVs, and heat pumps reshape both supply and demand patterns. Grid congestion has become one of the most pressing challenges for utilities navigating this transition. Accurate short-term load forecasting is essential—not only for congestion management, but also for transport forecasts, EV charging capacity estimation, and grid loss prediction.
OpenSTEF is an open-source Python package that provides accurate short-term forecasting for all of these use cases. As demand grows beyond congestion management, we have been working with the community on a major redesign to make it more flexible and easier to adopt across different contexts and user types—from researchers and small-scale teams to large-scale deployments within complex enterprise landscapes.
In this presentation, we will share the journey and architecture of the OpenSTEF V4 redesign, how we did it, the lessons we learned, and a sneak peek of the features of the current alpha release.
To learn more about OpenSTEF, visit: https://www.lfenergy.org/projects/openstef/
Speakers
Bart Pleiter is a Data Science Software Engineer at Alliander, a distribution system operator (DSO) in the Netherlands. Alliander provides reliable, affordable, and accessible energy transport and distribution to a large part of the Netherlands. To provide insights into the load on the electricity grid for the next 48 hours, Bart helps building and maintaining smart data-driven software, such as OpenSTEF. As for OpenSTEF, Bart is mostly involved in the technical development.
Egor Dmitriev works as a Software Engineer at Alliander, a distribution system operator in the Netherlands. At Alliander, I contribute to OpenSTEF, an open source short term forecasting solution for electricity grid load prediction. My work focuses on machine learning infrastructure, data engineering, and ensuring data quality in production ML pipelines.
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