OpenSTEF Short-Term Forecasting Helps Reduce Carbon Emissions скачать в хорошем качестве

OpenSTEF Short-Term Forecasting Helps Reduce Carbon Emissions

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/ope... 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.