Delft AI Energy Lab

Hello World! We are the Delft-AI-Energy Lab at TU Delft. Here, we aim to investigate how new AI methods can contribute to the management of dynamic energy systems. Follow us here in twitter and our website for news, events, and exciting AI research in our Lab! #DelftAIEnergyLab

Our paper, "A hybrid curriculum learning and tree search approach for network topology control" is accepted in EPSR journal! 🎉 sciencedirect.com/science/articl… The hybrid CL and MCTS approach improves RL training and efficiency, and finds long-term strategies. ⚡️

Cheers to an amazing night out with our group! 🍻

Our lab co-founder, Jochen L. Cremer, has been promoted to Associate Professor! 🎉 We're thrilled to celebrate this well-deserved milestone and are grateful for his inspiring leadership!

Haiwei Xie presenting her research "Game-Theoric Learning for Power System Dynamic Ancillary Service Provisions" at @IEEECDC2024 Milan last week. Paper: ieeexplore.ieee.org/abstract/docum…

🎓 Congratulations to Thomas Lautenbacher on successfully defending his master's thesis, titled "Multi-objective reinforcement learning for power grid topology control"!🌟

🎓 Congratulations to Viviana Kleine on successfully defending her master's thesis, titled "Utilizing Machine Learning for the Prediction of the Hysteresis Factor in Lithium-Ion Batteries Through Incorporation of Driving Cycles"!🌟

With her prior experience in both academic research and the energy industry, Betül brings a valuable interdisciplinary perspective to the lab. Driven by a commitment to the energy transition, she aims to contribute to the advancement of sustainable & smart energy solutions. (3/3)

Betül previously obtained her MSc in Data Science from Loughborough University and her BSc in Industrial Engineering, with a specialization in Electrical Power Engineering, from the University of Duisburg-Essen, where she was recognized as the best graduate of her year. (2/3)

We are excited to welcome Betül Mamudi, who joined our lab as a PhD candidate last month! Her research focuses on graph-based machine learning to energy system state estimation, in collaboration with industry leader @Alliander within the @IC4AI AI for Energy Grids Lab. ...(1/3)

Runyao Yu presenting his research on “Predicting Battery Cycle Life with Few-Short Transfer Learning over Heterogeneous Datasets” at the @ieee_pes #ISGT-Europe 2024 in Dubrovnik.

... The proposed approach speeds up LODF calculations for N-k outages, enhancing the reliability and efficiency of power systems amid increasing extreme weather events. ⚡🌍

Our paper, "Polynomial Line Outage Distribution Factors for Estimating Expected Congestion and Security," is accepted in IEEE Transactions on Power Systems! 🎉 ieeexplore.ieee.org/abstract/docum… ...

🎥Demetris explaining his research on "Estimating the flexibility in distribution networks to support TSO-DSO operation". ⚡ #DAIELab_members

Check out Demetris' latest paper on "Tensor Convolution-Based Aggregated Flexibility Estimation in Active Distribution Systems" in IEEE TSG! 🌟 ieeexplore.ieee.org/document/10663…

🎥Our PhD candidate Ali Rajaei on "Machine Learning for Transmission Network Topology Optimization". 🌟The AI model speeds up solving the topology optimization and identifies busbar splitting actions to relieve grid congestion. ⚡ #DAIELab_members

🎥Our PhD candidate Olayiwola Arowolo on "Accelerating EMT Simulations in Low Inertia Power Grids with ML". 🌟 The AI model assesses numerous scenarios and automates EMT simulations to find optimal set points, preventing instability and power quality issues! ⚡ #DAIELab_members

3- 𝐏𝐨𝐬𝐭𝐝𝐨𝐜 𝐀𝐩𝐩𝐥𝐢𝐞𝐝 𝐏𝐫𝐨𝐛𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐓𝐡𝐞𝐨𝐫𝐲 𝐟𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐓𝐚𝐬𝐤𝐬 𝐢𝐧 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 lnkd.in/eFnUD9uA

2- 𝐏𝐡𝐃 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 𝐌𝐚𝐫𝐤𝐨𝐯 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐟𝐨𝐫 𝐏𝐨𝐰𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 𝐓𝐨𝐩𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 lnkd.in/eaQuPudF

1- 𝐏𝐡𝐃 𝐏𝐨𝐬𝐢𝐭𝐢𝐨𝐧 "𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐨𝐫 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐒𝐭𝐚𝐭𝐞 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐢𝐨𝐧" 𝐚𝐬 𝐩𝐚𝐫𝐭 of our @ic4ai ICAI AI for Energy Grids Lab with @Alliander. linkedin.com/jobs/view/3972… ...

🚨 Exciting News! 🚨 We have new #PhD and #Postdoc openings! 🌟 We invite all qualified candidates to apply! Click the links below for more info. ⬇️