Update support md#27
Open
JanMaartenvanDoorn wants to merge 4 commits into
Open
Conversation
Member
JanMaartenvanDoorn
commented
May 29, 2026
JanMaartenvanDoorn
commented
- update docs
MvLieshout
reviewed
May 29, 2026
| The energy transition poses new challenges to all parties within the energy sector. Grid operators, grappling with the upsurge in renewable energy and heightened electrification, find their grid capacities nearing physical limitations. Therefore, it is imperative to forecast grid load in the upcoming hours to days, enabling the anticipation of local congestion and thereby optimal utilization of existing assets. | ||
|
|
||
| OpenSTEF provides a complete software stack specifically engineered to forecast the load on the electricity grid for the next hours to days. Given a timeseries of measured (net) load or generation, a fully automated machine learning pipeline is executed which delivers a probabilistic forecast of future load. This is applicable to energy consumption, renewable generation, or a combination of the two. OpenSTEF does not stop at forecating: it validates input data, combines measurements with external predictors such as weather data and market prices, trains any scikit-learn compatible machine learning model, and delivers the forecast via both an API and an (expert) graphical user interface. The entire stack, crafted on open-source technology and adhering to standards, is organized in a microservice architecture optimized for cloud-deployment. | ||
| OpenSTEF provides a complete software stack specifically engineered to forecast the load on the electricity grid for the next hours to days. Given a timeseries of measured (net) load or generation, a fully automated machine learning pipeline is executed which delivers a probabilistic forecast of future load. This is applicable to energy consumption, renewable generation, or a combination of the two. OpenSTEF does not stop at forecasting: it validates input data, combines measurements with external predictors such as weather data and market prices, trains any scikit-learn compatible machine learning model, and delivers the forecast via both an API and an (expert) graphical user interface. The entire stack, crafted on open-source technology and adhering to standards, is organized in a microservice architecture optimized for cloud-deployment. |
There was a problem hiding this comment.
This part is outdated, especially the last part is more about OpenSTEF v3. We do not provide a GUI anymore.
Suggested change
| OpenSTEF provides a complete software stack specifically engineered to forecast the load on the electricity grid for the next hours to days. Given a timeseries of measured (net) load or generation, a fully automated machine learning pipeline is executed which delivers a probabilistic forecast of future load. This is applicable to energy consumption, renewable generation, or a combination of the two. OpenSTEF does not stop at forecasting: it validates input data, combines measurements with external predictors such as weather data and market prices, trains any scikit-learn compatible machine learning model, and delivers the forecast via both an API and an (expert) graphical user interface. The entire stack, crafted on open-source technology and adhering to standards, is organized in a microservice architecture optimized for cloud-deployment. | |
| OpenSTEF provides a complete software stack specifically engineered to forecast the load on the electricity grid for the next hours to days. Given a timeseries of measured (net) load or generation, a fully automated machine learning pipeline is executed which delivers a probabilistic forecast of future load. This is applicable to energy consumption, renewable generation, or a combination of the two. OpenSTEF does not stop at forecasting: it validates input data, combines measurements with external predictors such as weather data and market prices, trains any scikit-learn compatible machine learning model, and produces the forecast. |
MvLieshout
reviewed
May 29, 2026
| OpenSTEF provides a complete software stack specifically engineered to forecast the load on the electricity grid for the next hours to days. Given a timeseries of measured (net) load or generation, a fully automated machine learning pipeline is executed which delivers a probabilistic forecast of future load. This is applicable to energy consumption, renewable generation, or a combination of the two. OpenSTEF does not stop at forecasting: it validates input data, combines measurements with external predictors such as weather data and market prices, trains any scikit-learn compatible machine learning model, and delivers the forecast via both an API and an (expert) graphical user interface. The entire stack, crafted on open-source technology and adhering to standards, is organized in a microservice architecture optimized for cloud-deployment. | ||
|
|
||
| The Dutch DSO Alliander started the Short-Term-Forecasting project to anticipate congestion in the distribution grid, to allow for grid safety analysis in the transmission grid and to enable smart grid innovations to locally balance supply and demand within the constraints of the grid. The objective of opensourcing the stack is two-fold: provide an industry standard for generating and evaluating forecasts in the operational time-domain, as well as allow for structured collaboration. | ||
|  |
There was a problem hiding this comment.
We do not provide this dashboard anymore. Maybe just remove it for now? We could later add some plots from BEAM.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.