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.

The team


PhD candidates

Olayiwola Arowolo

PhD candidate

Basel Morsy

PhD candidate

Viktor Zobernig

PhD candidate



  1. EE4C12 Machine Learning for Electrical Engineering, 5 ECTS, EEMCS, Electrical Engineering MSc Program"?
  2. MOOC Digitalisation of Intelligent and Integrated Energy Systems
  3. SC42150 Statistical Signal Processing, 3 ECTS, 3ME, Systems & Control MSc program
  4. SC42110 Dynamic Programming & Stochastic Control, 5 ECTS, 3ME, Systems & Control MSc program


Master projects


  1. AC Optimal Data Generation (ACODG) for Power System Security
  2. Graph Neural Networks for Security-Constrained Optimal Power Flow
  3. Estimating the Flexibility Evolution in Active Distribution Grids
  4. Neural Ordinary Differential Equations for Power System Dynamics
  5. Optimal PMU Placement for Flexibility and State Estimation
  6. Non-intrusive Load Monitoring of the Electricity Consumption
  7. Coordinated Control of Virtual Power Plants for Frequency Stability
  8. Offering Strategies of Virtual Power Plants in Ancillary Service Markets Based on Stochastic Programming

See also DAI Energy lab MSc Cataloge.pdf


Applied AI projects

  1. Data-Driven Adaptive Dynamic Equivalents of Active Distribution and Transmission Networks, Alex Neagu, TU Delft
  2. End-to-end learning for N-k SC-OPF, Bastien Giraud, TU Delft, NTNU, Norway
  3. Neural Ordinary Differential Equations for Frequency Dynamics, Nila Krishnakumar, TU Delft
  4. Reinforcement learning for transmission network topology control, Geert Jan Meppelink, TU Delft, NTNU, Norway
  5. Offering Strategies of Virtual Power Plants in Ancillary Service Markets Based on Stochastic Programming, Torben Zeller, RWTH Aachen University, Germany
  6. Market Mechanism Design for Virtual Inertia, Johnny Zheng
  7. Energy Community Management with Reinforcement Learning, Catarina Santos Neves, Instituto Superior Técnico, Portugal

Fundamental AI projects

  1. Non-intrusive load monitoring for residential houses
  2. Scalable dictionary learning to learn high-level features
  3. Sample-efficient learning for automated theorem proving in higher-order logic, Honours Programme Bachelor Project Proposal 2022-2024
  4. Learning Drivers’ Preferences in Delivery Route Planning through Inverse Optimization
  5. Fault Detection and Isolation of Nonlinear Systems with Optimized Model Mismatch
  6. Robustness in Fault Diagnosis applied in the Lateral Control of Automated Vehicles


  1. Multivariable Anomaly Detection Framework for Multi-sensor Network, TU Delft, 2022
  2. Transferring Domain Knowledge to Data-driven Controller, TU Delft, 2022
  3. Multi-Modal End-to-End Learning for Real-Time Monitoring of Sustainable Energy Systems, TU Delft, 2022
  4. Deep Statistical Solver for Distribution System State Estimation, Benjamin Habib, TU Delf, Stedin, 2022
  5. Meter placement for state estimation in distribution networks, Sattama Datta, TU Delft, Alliander, 2022
  6. End-to-End Learning for Sustainable Energy Scheduling, Dariush Wahdany, TU Delft, RWTH Aachen University, 2021
  7. Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response, Jasper van Tilburg, TU Delft, 2021
  8. On the Road from Active Inference to Regret Minimization, TU Delft, 2021
  9. Conjugate Dynamic Programming, TU Delft, 2021
  10. Tractable Algorithms for Large Scale Mixed Integer Quadratic Programming: A Principal Component Analysis Approach, TU Delft, 2021
  11. Learning Parametric Mixed Integer Quadratic Programming via Inverse Optimization, TU Delft, 2021
  12. On Complexity of Data-driven Controls in Stochastic Environments, TU Delft, 2021



Dit onderdeel wordt voor u geblokkeerd omdat het cookies bevat. Wilt u deze content (en anderen) alsnog bekijken? Door hier op te klikken geeft u alsnog toestemming voor het plaatsen van cookies.