KISSKI - 350_3-08_ki-modelle-energie

Target group

Companies (of any size) and research institutes with applications in the field of of energy

Your requirements

You need consulting support for your use case in the field of energy regarding the

selection of AI methods
development or validation of AI models
transfer of AI models into operational use
troubleshooting or optimization of an existing AI model

Our offer

We offer support with questions relating to the use of AI models in the field of energy. The possible applications of AI in energy-related issues are diverse and require a wide variety of AI methods. Possible problems range from anomaly detection or predictive maintenance from large amounts of data, forecasts of power generation or consumption, or simulations and optimization of the power grid through to the optimization of energy storage systems. AI methods can however also help with the optimal selection of locations for wind turbines, the storage and conversion of renewable energies or the design of more energy-efficient buildings. Our services include:

Advice on the selection of suitable AI methods for the respective use case
Advice on issues relating to the development, training and validation of AI models
Consulting support in the planning and implementation of studies regarding AI models
Advice on issues relating to the transfer and use of AI models in operations
Advice on troubleshooting and pitfalls in dealing with AI models

Requirements

Energy-related use case of AI

Success stories

As part of the Competence Center for Cognitive Energy Systems, innovative AI solutions for applications in the energy sector have been developed in numerous spotlight projects:

https://kognitive-energie-systeme.de/spotlights/

The successful use of AI methods for power consumption forecasts was recognized by the German Energy Agency (dena) by awarding first place in the Data4Grid Challenge to our team at Fraunhofer IEE:

https://www.iee.fraunhofer.de/de/presse-infothek/Presse-Medien/2022/verbrauchsprognosen_ausgezeichnet.html

Another example of the versatility of AI applications in the energy context is the VemoSat demonstrator, which uses AI to increase resolution of satellite images and thus supports vegetation monitoring along energy infrastructures:

https://maps.iee.fraunhofer.de/vemosat/

By combining AI and physical boundary conditions, we are able to offer explainable and transparent power flow forecasts, which is an important step towards providing grid operators with a complete forecasting solution:

https://www.iee.fraunhofer.de/de/geschaeftsfelder/energiemeteorologische-informationssysteme/gridfox-leistungsflussprognosen.html

The WindGISKI research project is developing an AI-based information system for selecting optimial areas for wind turbines:

https://www.windgiski.uni-hannover.de/de/

The "Institute fuer Entwerfen und Konstruieren" at Leibniz Universität Hannover is researching AI methods that can be used to assist with more sustainable and energy-efficient building designs: