This is the main project repository related to the FFG-funded project "transpAIrent.energy", led by AIT Austrian Institute of Technology GmbH ("AIT") (specifically at the Center for Energy), in collaboration with Projektplanungs- Beratungs- und Entwicklungs GmbH ("pbeg"), B-SEC better secure KG ("B-SEC"), and UBIMET GmbH ("UBIMET").
This project is conducted within the framework of the "AI for Green 2023" call by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK). It is administered on behalf of the BMK by the Austrian Research Promotion Agency (FFG).
The project's website is online here: transpAIrent.energy
| FFG call | Digitale Technologien, AI for Green 2023 |
| Project begin | 01.05.2024 |
| Project end | 30.04.2027 |
| Project coordinator | AIT |
| Current project lead | Klara Maggauer |
| Project partners | pbeg, B-SEC, UBIMET |
The project transpAIrent.energy - Transparent AI Forecasts for Green Energy in Austria aims to employ generative artificial intelligence (AI) methods to create probabilistic live forecasts for variables relevant to the energy system – such as electricity prices and CO2 intensities – in Austria and to develop a transparent platform that makes them publicly accessible. Furthermore, these forecasts will be used in the project to develop an optimization strategy for the operation of flexible renewable energy systems under a variety of environmental and system-relevant requirements. This “multi-objective” optimization strategy simultaneously brings economic benefits, which create incentives, and guarantees lower CO2 intensity, thus promoting the transition to a more environmentally friendly energy system.
The project specifically pursues the following goals:
- Development of an innovative generative AI-based algorithm for creating probabilistic forecasts for variables relevant to the energy system and publishing them live on a transparent platform;
- Using these forecasts to optimize flexible renewable energy systems to make their operation both more economical and more sustainable.
These goals are achieved through four dedicated work packages (WPs) within the project, which focus on
- data collection, processing, and documentation as well as the creation of weather forecasts,
- development of AI-based forecasting algorithms,
- platform development and implementation,
- and method validation (“proof of concept”) through simulation and live testing.
These content-related APs are moreover supplemented by a project management WP and a dissemination and exploitation WP. The latter aims to highlight and disseminate the added value of the project for Austria through the transparent live forecast platform as well as for a more sustainable energy future through the optimized operation of renewable energy flexibilities and to underscore the possibilities for exploiting the project results.
Keywords: generative AI; probabilistic forecasting; transparency platform; multi-objective optimization; proof of concept