Spotlights illuminate paths towards intelligent energy systems


Deep Energy Trade – Automated electricity trading for all

Trading in energy has so far been a domain for pros. This could change with the help of machines. Other stakeholders might then also become more interested in the electricity exchange. In the Deep Energy Trade project, a demonstrator shows how intelligent automated electricity trading can work. The self-learning agent is capable of developing [...]


ARCANA – Wind turbines monitor themselves

Errors can happen – but it is important to recognize them. This also applies to the operation of wind power plants. Major damage and failures can be disastrous for the operators of such plants. The anomalies detected by monitoring systems often only indicate that a potential error is present or may occur. In the [...]


Vertical load forecasting: Going with the flow

The bottom line must be zero – this applies to power input and consumption at all times. To establish this equilibrium, power grid operators calculate load flows in advance for the next few hours. In this process, the load at the transition points between different electrical grids represents a particular challenge. An innovative approach [...]


AAE – Adversarial attacks in the energy sector

The project involved investigating the robustness of an AI-based wind power forecasting model against intentional but imperceptible changes in input data aimed at falsifying the output data. The use of AI-based methods in critical infrastructures such as the energy sector can lead to potential security issues. For instance, adversarial attacks are a major threat. [...]


GRADS – Generating artificial district heating supply systems

Due to the limited number of district heating supply systems in Germany and their individuality, their data are difficult to use for research purposes and unpublishable. The sensitive data reveals the affiliation to specific providers, posing a risk of abuse. Artificial intelligence, specifically deep reinforcement learning (DRL), is used for research purposes to create [...]



Testing machine learning algorithms for intelligent distributed charging management at company locations. What is the problem? Avoiding load peaks to reduce load peaks and power price costs and meeting the requirements of the grid operator. Current technologies are limited in particular in terms of costs, flexibility, and complexity management. What problems emerge? Simultaneous charging [...]



Forecasting power flows in the electrical grid using graph neural networks: Forecasting of power flows in the electrical grid enables grid operators to make advance grid calculations to recognize bottlenecks in good time and take countermeasures. Machine learning approaches using local forecasting models are not capable of considering dependencies between individual system components (such [...]

“Artificial intelligence is a key technology for the ongoing development of the energy turnaround.”

Angela Dorn, Hessian Minster of Higher Education, Research and the Arts

“Artificial intelligence is a central element of tomorrow’s economy – and an important element for the sustainable transformation of our energy system.”

Kerstin Andreae, Managing Director of the German Association of Energy and Water Industries (BDEW)