OpenSTEF LF Energy Website (2026)

Source: OpenSTEF project website (openstef.energy), the LF Energy project listing, and the LF Energy Confluence wiki page. The pages are undated, so published is the 2026-09-22 capture date.

Summary

OpenSTEF is an open-source Python package providing machine-learning pipelines for short-term energy forecasting, hosted at LF Energy (the Linux Foundation’s energy-sector open-source umbrella). It originated at Dutch DSO Alliander’s Short-Term-Forecasting project, built specifically to anticipate distribution-grid congestion, support transmission-grid safety analysis, and enable local supply/demand balancing within grid constraints.

Key claims

• Generates probabilistic forecasts from hours to days ahead for any energy signal; documented DSO use cases include proactive congestion management, communicating expected energy exchange with other grid operators, and predicting grid losses.
• Alliander open-sourced the stack with the explicit aim of establishing an industry standard for generating and evaluating forecasts, and enabling structured collaboration, rather than every DSO building or buying forecasting in isolation — framed on the current site as “stop building the same tool twice.”
• Alliander runs OpenSTEF in production at thousands of grid locations, as input for congestion management, communicating expected energy exchange with the transmission system operator, and estimating grid losses.
• Swedish/Nordic adoption: Sigholm (Sweden) has adopted OpenSTEF as the forecasting engine behind its Aurora production-optimization platform for district heating and combined heat and power. Aurora generates ~20,000 forecasts/week (~4 seconds per forecasting cycle) and, per the site, optimizes approximately 40% of Sweden’s district heating production. This is a district-heating/thermal use case, not an electricity-DSO congestion-management deployment — the Alliander pattern remains the only documented electricity-DSO production use.

Relevance to the wiki