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The OpenSTEF project provides a valuable portfolio of automated, Open Source machine learning pipelines that provide accurate short-term load forecasting for energy grids.

Welcome to OpenSTEF: Short Term Energy Forecasting

Developed by energy experts and constantly refined as part of an open industry development process, the OpenSTEF forecasting pipelines combine inputs of measured historical grid data, together with relevant external predictors such as weather and market pricing. Users can forecast the future load on any grid, with the ability to look at energy consumption, (renewable) generation, or a combination of both.

OpenSTEF outputs forecasts via either an API or (expert) graphical user interface. The entire technology stack is built on Open Source technology and open standards, and its microservice architecture is optimized for cloud-native deployment.

Why OpenSTEF?

The energy transition is causing both a rapid growth in net grid loads and fundamental changes in the patterns of power supply and distribution. Mitigating grid congestion has become the number one mission critical challenge for utilities providers who are managing this change across legacy grid networks.

Accurate forecasting of grid loads forms a vital part of any mitigation strategy. However, the combination of changing demand patterns, together with erratic supply from more distributed, weather-dependent, renewable energy sources, has caused an exponential increase in complexity. On this synchronous, renewable grid: microgeneration, microstorage, feeder tariffs and dynamic pricing also mean that consumers can become suppliers, and both demand and supply can be manipulated through price. This has generated a pressing need for more sophisticated forecasting tools for energy demand, supply and grid load

OpenSTEF is a collaborative Open Source project from within the energy industry to develop more accurate short term forecasting tools to address these challenges.

At Alliander OpenSTEF is used for congestion management and prohibiting exceedance of grid limitations. However, there are also other uses for OpenSTEF. Solar farms can use it to forecast for peak shaving. Other use cases could be an aggregator steering their customers to adhere to a contract, forecasting as a service, or an energy trader who implements it to maximize returns on a specific market.

How OpenSTEF Works

To generate new forecasts, users input a timeseries dataset of measured (net) load and/or generation for any existing grid. The OpenSTEF pipelines then perform validation of the input data, before using machine learning to combine the historical measurements with external predictors such as weather data and market prices.

The pipeline can train a model and subsequently output forecasts of up to 48 hours, outlining probabilistic grid loads and with the ability to detail both demand and supply from localized generation and storage.

OpenSTEF is a library built on open standards and its fully Open Source technology stack is designed to be flexible, customizable and extensible. Forecasts are delivered into a Grafana dashboard and can be easily integrated into broader systems. The pipelines are open, well-documented and can be used to train and incorporate any scikit-learn compatible machine learning model.

Collaboration

Join our growing OpenSTEF community:

Key Features of OpenSTEF

  • Operational Forecasts: Accurate forecasts enable utilities to anticipate congestion and plan resource allocation more effectively.
  • Real-time Data Analysis: OpenSTEF leverages real-time data from grid sensors and weather forecasts to provide timely insights.
  • Customizable Solutions: OpenSTEF offers customizable solutions tailored to the unique needs of each utility, ensuring maximum efficiency and effectiveness.
  • Infused expert energy knowledge: build-in energy knowledge to improve your forecasting quality. For example, additional weather features are automatically calculated and implemented.

Learn More

Explore how OpenSTEF is driving innovation in grid management and delivering tangible results for utilities like Alliander. Visit the external information sources below to learn more about our initiatives and discover how OpenSTEF can transform your grid operations.

OpenSTEF Case Studies

OpenSTEF Videos

A lot of renewable production and changes in the consumption of generation are causing the loads on the grid to fluctuate more, making forecasts more difficult to accurately predict. OpenSTEF, one of LF Energy’s latest open source projects, helps deliver self-correcting and accurate forecasts of the load on the grid and either energy consumption or generation from renewable sources.

In this episode of TFiR State of Energy, Swapnil Bhartiya sits down with Jonas van den Bogaard, Solution Architect at Alliander, and Frank Kreuwel, Product Owner and Data Scientist at Alliander, to discuss their OpenSTEF project and how it aims to help energy companies. 

Kreuwel says, “The grid that's currently in place actually was installed decades ago. And decades ago, electric vehicles, or putting solar panels on your house or wind turbines somewhere in the field really wasn't a thing. It didn't exist yet. And so the grid that we put into the ground 30 years ago wasn't designed to have this both inflow and also outflow of energy.”

Key highlights of this video are:
Changes in the consumption of the generation are making it more difficult to forecast the load on the grid. Having accurate predictions of the load on the grid, especially for the short-term, is discussed. 
The grid that was implemented 30 years ago was not designed for today’s energy needs. Kreuwel discusses some of the challenges this creates. 
Alliander already uses OpenSTEF internally for three main goals for use cases. Kreuwel discusses these goals and how it is being used in Alliander in further detail in the video. 
Bogaard discusses why Alliander felt it was important to open source the OpenSTEF project and what benefits it believes this will bring to the community as well as to Alliander.
OpenSTEF is one of the key systems used by Alliander to make forecasts for the national grid operator, to manage and balance the grid and to deploy new smart solutions. Kreuwel discusses how mature the project is and how Alliander are making it usable beyond the scope of Alliander use cases. 
Organizations in the energy sector who need accurate forecasts would be interested in this project, but Kreuwel discusses how the project could be useful from an academic standpoint as well. 
Those wanting to become a part of the community and collaborate can go to https://www.lfenergy.org/projects/openstef/ for further information or check out the Github community at GitHub.com/OpenSTEF.
Kreuwel discusses the process of how new features are assessed and introduced. He explains how new versions of the OpenSTEF package are released and how the roadmap is determined by each contributor from their own point of view. 
Alliander uses as many open source packages as makes sense, such as the machine learning pipelines and machine learning algorithms. Kreuwel explains some of the open source components used in the OpenSTEF project. 
It is becoming increasingly important for organizations to reduce their carbon emissions and carbon footprint. Kreuwel explains how the OpenSTEF project is helping organizations achieve this. 
Alliander wants to enable the transition to more renewables, getting the most out of the grid while making sure we use all the capacity in a grid. Kreuwel explains how the tooling from OpenSTEF is helping create a new level of distribution system operator with accurate assessment on capacity and forecasting.
Kreuwel explains how short-term forecasting helps organizations manage things in real-time but while long-term analysis goes hand in hand with this, they are two separate applications. He explains why this is the case. 19:42

A lot of renewable production and changes in the consumption of generation are causing the loads on the grid to fluctuate more, making forecasts more difficult to accurately predict. OpenSTEF, one of LF Energy’s latest open source projects, helps deliver self-correcting and accurate forecasts of the load on the grid and either energy consumption or generation from renewable sources.

In this episode of TFiR State of Energy, Swapnil Bhartiya sits down with Jonas van den Bogaard, Solution Architect at Alliander, and Frank Kreuwel, Product Owner and Data Scientist at Alliander, to discuss their OpenSTEF project and how it aims to help energy companies.

Kreuwel says, “The grid that's currently in place actually was installed decades ago. And decades ago, electric vehicles, or putting solar panels on your house or wind turbines somewhere in the field really wasn't a thing. It didn't exist yet. And so the grid that we put into the ground 30 years ago wasn't designed to have this both inflow and also outflow of energy.”

Key highlights of this video are:
Changes in the consumption of the generation are making it more difficult to forecast the load on the grid. Having accurate predictions of the load on the grid, especially for the short-term, is discussed.
The grid that was implemented 30 years ago was not designed for today’s energy needs. Kreuwel discusses some of the challenges this creates.
Alliander already uses OpenSTEF internally for three main goals for use cases. Kreuwel discusses these goals and how it is being used in Alliander in further detail in the video.
Bogaard discusses why Alliander felt it was important to open source the OpenSTEF project and what benefits it believes this will bring to the community as well as to Alliander.
OpenSTEF is one of the key systems used by Alliander to make forecasts for the national grid operator, to manage and balance the grid and to deploy new smart solutions. Kreuwel discusses how mature the project is and how Alliander are making it usable beyond the scope of Alliander use cases.
Organizations in the energy sector who need accurate forecasts would be interested in this project, but Kreuwel discusses how the project could be useful from an academic standpoint as well.
Those wanting to become a part of the community and collaborate can go to https://www.lfenergy.org/projects/openstef/ for further information or check out the Github community at GitHub.com/OpenSTEF.
Kreuwel discusses the process of how new features are assessed and introduced. He explains how new versions of the OpenSTEF package are released and how the roadmap is determined by each contributor from their own point of view.
Alliander uses as many open source packages as makes sense, such as the machine learning pipelines and machine learning algorithms. Kreuwel explains some of the open source components used in the OpenSTEF project.
It is becoming increasingly important for organizations to reduce their carbon emissions and carbon footprint. Kreuwel explains how the OpenSTEF project is helping organizations achieve this.
Alliander wants to enable the transition to more renewables, getting the most out of the grid while making sure we use all the capacity in a grid. Kreuwel explains how the tooling from OpenSTEF is helping create a new level of distribution system operator with accurate assessment on capacity and forecasting.
Kreuwel explains how short-term forecasting helps organizations manage things in real-time but while long-term analysis goes hand in hand with this, they are two separate applications. He explains why this is the case.

YouTube Video UExLeUZmMUo5WGtwc0Z6bkstY2R6TGJid3ZHbVVldHNCTC4yODlGNEE0NkRGMEEzMEQy

OpenSTEF Short-Term Forecasting Helps Reduce Carbon Emissions

April 26, 2022 1:28 pm

FOSDEM 2024 Energy Devroom - OpenSTEF: Opensource Short Term Energy Forecasting

February 28, 2024 9:02 pm

Recent OpenSTEF News

Project Special Interest Group: Grid Simulation and Modeling

Project Lifecycle Stage: Incubation