Delft AI Energy Lab

AI for sustainable, reliable and effective energy systems

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.

The Delft AI Energy Lab is part of the TU Delft AI Labs programme.

Advanced AI-based mathematical models together with scalable algorithms offer reliable diagnosis and predictive tools for modern energy systems.

Making optimization algorithms computationally efficient matters in many applications including power systems

Describing experiment methodology.

Projects

Publications

Code & Datasets (Github)

The team

Directors

Jochen Cremer

Faculty of EEMCS
Lab director

Peyman Mohajerin Esfahani

Faculty of ME
Lab director

PhD candidates

Olayiwola Arowolo

PhD candidate

Mohammad Boveiri

PhD candidate

Demetris Chrysostomou

PhD candidate

Mert Karaçelebi

PhD candidate

Shabnam Khodakaramzadeh

PhD candidate

Basel Morsy

PhD candidate

Ali Rajaei

PhD candidate

Haiwei Xie

PhD candidate

Viktor Zobernig

PhD candidate

Perine Cunat

PhD researcher

Runyao Yu

PhD researcher

Luca Hofstadler

PhD researcher

Vacancies

We have currently several PhD positions available within the Delft AI Energy Lab

Postdocs

Jochen Stiasny

Education

Courses

2021/2022

2020/2021

2019/2020

Master projects

Ongoing

Applied AI projects

Distribution System State Estimation with Graph Neural Networks
ML for Dynamics in power systems
Algorithms for Distribution Grid Expansion
Reinforcement Learning for Congestion Management
Incentive-Based Community Management with Network Constraints
Stable Diffusion for Energy-system Optimized Solar Nowcasting
User-centric EV charging cycles to maximise battery lifetime

Fundamental AI projects

Non-intrusive load monitoring for residential houses
Scalable dictionary learning to learn high-level features
Multivariable Anomaly Detection Framework for Multi-sensor Network
Fault Detection and Isolation of Nonlinear Systems with Optimized Model Mismatch
Robustness in Fault Diagnosis applied in the Lateral Control of Automated Vehicles
Transferring Domain Knowledge to Data-driven Controller
Associated projects
MSc Project proposal: Correlations for the hydrodynamics of spouted bubbling fluidized beds uzing CFD, ANN's & ecperiments

Finished

“Reinforcement Learning for Coordinating Energy communities”, Catarina Santos Neves, TU Delft in collaboration with Instituto Superior Técnico, Lisbon, Portugal
“Market mechanisms for Frequency Service provided by Dynamic Virtual Power Plants”, Torben Zeller, TU Delft in collaboration with RWTH Aachen University
“Quantum Computing for Power Systems” Hjalmar Lindstedt, TU Delft
“Market Mechanism Design for Virtual Inertia”, Johnny Zheng, TU Delft
“Reinforcement learning for transmission network topology control” Geert-Jan Meppelink, TU Delft, in collaboration with NTNU, Trondheim, Norway
“Neural Ordinary Differential Equations for Frequency Dynamics” Nila Krishnakumar, TU Delft
“End-to-end learning for N-k SC-OPF” Bastien Giraud, TU Delft, in collaboration with NTNU, Trondheim, Norway
“Data-Driven Adaptive Dynamic Equivalents of Active Distribution and Transmission Networks” Alex Neagu, TU Delft
End-to-End Learning for Sustainable Energy Systems with PV and Wind, Rushil Vohra, TU Delft
Meter Placement for Estimating Power System States, Sattama Datta, TU Delft thesis together with Alliander, DSO Netherlands
Graph Neural Networks for State Estimation, Benjamin Habib, TU Delft thesis together with Stedin, DSO Netherlands
Prediction of malfunctioning of PV panels with Machine Learning, Dion de Mooy, TU Delft
End-to-End Learning for Sustainable Energy Scheduling, Dariush Wahdany, TU Delft, RWTH Aachen University
Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response, Jasper van Tilburg, TU Delft
On the Road from Active Inference to Regret Minimization, TU Delft, 2021
Conjugate Dynamic Programming, TU Delft, 2021
Tractable Algorithms for Large Scale Mixed Integer Quadratic Programming: A Principal Component Analysis Approach, TU Delft, 2021
Learning Parametric Mixed Integer Quadratic Programming via Inverse Optimization, TU Delft, 2021
On Complexity of Data-driven Controls in Stochastic Environments, TU Delft, 2021

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