How does AI contribute to an efficient energy transition?

• How can AI be used to make the energy transition efficient and sustainable?
• How do AI systems have to be designed so that electricity consumers use them?
  
• What impacts does the use of AI have on energy consumption?
   

New Trends in energy demand modeling

Digitalisation has the potential to fundamentally change the way things are currently performed. While most digitalised processes are getting more and more efficient, not all of them are designed in a way that reduces energy consumption. New societal trends empowered by digitalisation such as shared economy, autonomous driving and low-carbon circular economy could lead to an increase in energy demand if they are not countered by measures with a strong focus on saving energy. The goal of the EU-funded NEWTRENDS project is to identify and quantify how new societal trends may affect energy demand. To achieve its goal, it will combine qualitative and quantitative cross-sectoral modelling and explore how energy demand models can be improved to represent new societal trends.

Artificial Intelligence for the integrated energy transition

The analysis report, written as part of the BMWi-funded project “EnerAI – using artificial intelligence to optimize the energy system”, aims to make the alluring term of AI more objective and place this in the context of the integrated energy transition. The analysis shows that AI can be used in a wide variety of applications in all areas of the energy sector and can make a major contribution in future to a more secure, climate-friendly and cost-efficient energy supply.  

Roadmap Energy Efficiency 2050

The “Roadmap Energy Efficiency 2050”, the main dialog forum of the German government, has the task of promoting the urgently required advances in energy efficiency. The roadmap is part of Germany's energy efficiency strategy and is intended to discuss paths towards achieving the reduction target for 2050 that span different sectors. Additional concrete instruments and measures to improve energy efficiency are developed together with representatives from science, industry and civil society. The German government's goal of reaching climate neutrality by 2050 sets the level of ambition of the roadmap process.

The Working Group on Digitalization, which is coordinated by Fraunhofer ISI, deals with the question of the energy demand of AI, among other things, and the energy saving potentials that AI makes possible.  

• Dena (2020): Artificial Intelligence – from Hype to Reality for the Energy Industry. Autoren: Kratochwill, Lisa; Richard, Philipp; Babilon, Linda; Rehmann, Felix; Mamel, Sara; Fasbender, Sebastian. Beteiligt: Klobasa, Marian; Pelka, Sabine; Plötz, Patrick; Einhellig, L.; Kappl, J.; Behrens, K. DENA-Analyse, Berlin: Deutsche Energie-Agentur.
  
• Klobasa, Marian; Plötz, Patrick; Pelka, Sabine; Vogel, Lukas (2019): Künstliche Intelligenz für die integrierte Energiewende. Einordnung des technologischen Status Quo sowie Strukturierung von Anwendungsfeldern in der Energiewirtschaft. Karlsruhe: Fraunhofer ISI.
  
• Klobasa, Marian; Plötz, Patrick; Pelka, Sabine; Vogel, Lukas (2019): Artificial intelligence for the integrated energy transition. Assessing the technological status quo and categorising fields of application in the energy system. Karlsruhe: Fraunhofer ISI.
  
• Brugger, H. I.; Eichhammer, W. (2019): Studying the impacts of digitalization and further societal trends on energy demand. In: european energy innovation, Autumn 2019, 22–24.