Dr. S.H. (Simon) Tindemans

Dr. S.H. (Simon) Tindemans



Simon Tindemans is een universitair docent in de Intelligent Electrical Power Grids groep. Hij doet onderzoek op het thema onzekerheid en risicomanagement voor elektriciteitsnetwerken, op het raakvlak tussen de elektrotechniek, (computationele) statistiek en informatica. 


Zie de Engelse versie van deze pagina (schakelen rechtsboven op deze pagina) voor een uitgebreide beschrijving van mijn onderzoek.  

Huidig team 
Postdocs: Mojtaba Moradi Sepahvand, Na Li, Amirreza Silani
Promovendi: Hazem Abdelghany, Roman Hennig, Ensieh Sharifnia, Nanda Panda, Kutay Bölat, Shaohong Shi
MSc-afstudeerders: Isabel Oosterhagen, Ruiqi Zhang

Voormalige teamleden

Promovendi (copromotor) MSc afstudeerprojecten
Scripties beschikbaar via  https://repository.tudelft.nl/islandora/search/?collection=education 
  • Lotte Zwart (2022, with Stedin), "Congestion forecasting using a custom loss function"
  • Thomas Georgiou (2022, with Qirion/Alliander), "Interpretation of the Machine Learning Fault Classification Model Results Using Explainable Features"
  • Kevin Dankers (2022), "Profit optimized machine learning aided electricity storage; Comparison between privately owned and neighborhood shared"
  • Archana Ranganathan (2021, with Qirion/Alliander), "Automatic Identification of Fault Types in the Distribution Network using Supervised Learning"
  • Sai Suprabhath Nibhanupudi (2021, with Phase to Phase), "State Estimation in Medium Voltage Distribution Networks"
  • Ramon Mengerink (2021), "Cross-Border Participation in Capacity Mechanisms"
  • Devendra Kulkarni (2020, with Qirion/Alliander), "Unsupervised Learning to Locate Weak Spots in the Medium Voltage Grid"
  • Jules Zweekhorst (2020, with TenneT), "The development of a two day ahead power forecasting model for an offshore windpark"
  • Julian Betge (2020, with Alliander), "Monte Carlo Sampling Techniques for the Efficient Estimation of Risk Metrics of a Stochastic Distribution Grid Power Demand Model"
  • Subhitcha Ramkumar (2020), "Real Time Market Based Control of Flexible Distributed Energy Resources"
  • Medha Subramanian (2020, with TenneT), "Optimising Grid Topology Reconfiguration using Reinforcement Learning"
  • María Miranda Castillo (2019, with ENTSO-E), "Evaluation of missing capacity and resource adequacy in an interconnected power system"
  • Shreyas Nikte (2019), "Investigation of active learning techniques for dynamic Time-of-Use (dToU) tariff policy design for residential users"
  • Jennie Christiaanse (2019, with HyTEPS), "Algorithm for Determining the Hosting Capacity of Independent PV, or EV Charger Systems"
  • Jonathan Budez (2019), "Quantifying the Contribution of distributed flexible loads to congestion management"
  • Sotiris Dimitrakopoulos (2019, with DEPsys), "Linear state estimation method for distribution grids"
  • Roberto Francica (2019, with Alliander), "Assessment of machine learning algorithms for the purpose of heat pump detection based on load profiles and temperature readings"
  • Cheng-Kai Wang (2018), "Urban building energy modeling using a 3D city model and minimizing uncertainty through Bayesian inference"
  • Rob de Nie (2018, with DWA), "Detecting a change in building electricity consumption patterns from electricity master meter data"

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