Jochen L. Cremer

Jochen L. Cremer

he/him

Electrical Power Engineer with Computer Science Expertise, PhD Imperial College London, MSc BSc BSc RWTH Aachen University

Projects

AI EFFECT: Testing and Experimentation Facilities for the energy sector – bringing technology to the market

As the digital age transforms the energy landscape, the integration of artificial intelligence (AI) into critical energy infrastructure is set to boost efficiency, resilience, and sustainability. To drive this innovation, the AI-EFFECT project has been launched, aimed at accelerating the development, testing, and validation of AI applications in the energy sector. The project will run until September 2027 and is funded by the European Union’s Horizon Europe programme, under agreement no. 101172952.

Research Programme on Power Systems Operation & Planning with AI: AIT and TU Delft

AI-based approaches have emerged to accelerate the transformation of our energy systems toward sustainability. With digitalisation revolutionising the energy sector, there is now a vast potential to achieve more efficient, reliable, and secure operation of our energy infrastructure. Artificial Intelligence (AI) has become a powerful and disruptive tool for decision-making, helping to tackle the increased complexity and uncertainty of the transition towards a sustainable and renewable energy system.

Delft AI Energy Lab

Energy systems are the backbone of our modern society. It is of great importance that these systems are sustainable, reliable and effective now and in the future. There is strong expertise in this field on the TU Delft campus. The Delft AI Energy Lab investigates how new AI-based methods can contribute to the management of dynamic energy systems. Therefore we combine groundbreaking machine learning with the reliable theory of the physical energy system. For example, it is possible with the AI technique ‘neural networks’ to model differential equations describing dynamics in areas such as fluid dynamics, and for predicting extreme, rare events. Delft AI Energy Lab investigates these promising methods for applicability for monitoring the ‘health’ of parts of energy systems, and for the early detection of threats.

GAIM: Graph-based AI Monitoring Tools for Complex-Systems

The project aims to develop a heterogeneous Graph Neural Network architecture for identifying and learning active topologies in distribution grids based on measurement data. This will address challenges related to varying topologies and provide a feasible solution within a guaranteed convergence time.

MEGAMIND Measuring, Gathering, Mining and Integrating Data for Self-management in the Edge of the Electricity System

Network operators and market parties are looking for ways to prevent intelligently overloading the network and to link supply and demand. The MEGAMIND programme brings together knowledge of energy systems, artificial intelligence and regulation to develop both the necessary technology and appropriate regulations. The researchers aim to develop models to predict when problems will arise. Then, they will have devices that consume energy interact directly with devices that produce energy to avoid these situations.

ROBUST Trustworthy AI-based Systems for Sustainable Growth ROBUST ICAI Project

ROBUST ‘Trustworthy AI systems for sustainable growth’ is a Long Term Program (LTP) from NWO. The robust program aims to achieve breakthroughs in five core dimensions of robust artificial intelligence (AI): accuracy, reliability, repeatability, resilience, and security. The reliability of an AI-based system is formalized through so-called contracts, that is, explicit guarantees about the intended behavior of the system. Explanations can bring intrinsic confidence to general users. Therefore, the development of explanation and evaluation methods is an essential part of this research.

NWO Veni: Physics-informed AI to avoid power blackouts in the energy transition

The energy grid of the future will use a complex network of small, sustainable energy sources, such as solar panels and wind turbines. Increased complexity will make the network vulnerable to disruptions, made still worse by the extreme weather events caused by far-reaching climate change. Sudden catastrophic power outages can take place that potentially last for months and span entire regions, with serious consequences for society. Effective countermeasures depend on understanding the causes of these blackouts quickly, thsi research uses artificial intelligence both to predict power outages and to identify and address effective solutions. By managing these risks, the research will help to accelerate the energy transition and protect society from the next pan-European power outage.