Frederik vom Scheidt

Frederik vom Scheidt

  • Karlsruhe Institute of Technology (KIT)
    Fakultät für Wirtschaftswissenschaften
    Institut für Wirtschaftsinformatik und Marketing
    Kaiserstraße 89, 76133 Karlsruhe

Tätigkeiten

Forschungsinteressen & -methoden

  • Optimierung von Wasserstoff-Supply-Chains und deren Integration in das Stromsystem
  • Data Analytics & Machine Learning auf Smart Meter Daten
  • Ökonomische Analysen von Stromtarifen und ihren Effekten

 

Vorlesungen, Übungen, Seminare

SS 2021

  • Seminar "Smart Grid and Energy Markets"

WS 2020/21

  • Seminar "Smart Grid and Energy Markets"
  • Seminar "Applied Research in Energy Economics"

SS 2020

  • Seminar "Smart Grid and Energy Markets"
  • Seminar "Applied Research in Energy Economics"

WS 2019/20

  • Seminar "Smart Grid Economics"

SS 2019

  • Vorlesung “Energy Market Engineering“
  • Seminar "Smart Grid Economics"

WS 2018/19

  • Übung “Smart Grid Applications”
  • Seminar "Smart Grid Economics"

 

Lebenslauf

Seit 10 / 2018 Wissenschaftlicher Mitarbeiter am Institut für Informationswirtschaft und Marketing (IISM), Forschungsgruppe "Smart Grids & Energy Markets"
04 / 2019 Forschungsaufenthalt am Massachusetts Institute of Technology (MIT), USA
09 / 2018 M. Sc. Wirtschaftsingenieurwesen, Karlsruher Institut für Technologie (KIT)

Master Thesis: Residential Electricity Tariff Design: A data-driven analysis of socio-economic impacts and implications

08 / 2018 Forschungsaufenthalt am Massachusetts Institute of Technology (MIT), USA
01 / 2017 Auslandsstudium an der Königlich Technischen Hochschule (KTH) Stockholm, Schweden
10 / 2015 Studentische Hilfskraft für Smart Grid Integration, Forschungszentrum Informatik
08 / 2015 Studentische Hilfskraft für Dezentrale Energiesysteme und Netze, KIT
04 / 2015 B. Sc. Wirtschaftsingenieurwesen, KIT

Bachelor Thesis: Development and implementation of a charging strategy that reduces CO2 emissions caused by electric cars in France and Germany

Publikationsliste


Probabilistic Forecasting of Household Loads: Effects of Distributed Energy Technologies on Forecast Quality.
vom Scheidt, F.; Dong, X.; Bartos, A.; Staudt, P.; Weinhardt, C.
2021. e-Energy ’21: The Twelfth ACM International Conference on Future Energy Systems Virtual Event Italy 28 June, 2021- 2 July, 2021, 231–238, Association for Computing Machinery (ACM). doi:10.1145/3447555.3464861
The effects of electricity tariffs on cost-minimal hydrogen supply chains and their impact on electricity prices and redispatch costs.
Vom Scheidt, F.; Qu, J.; Staudt, P.; Mallapragada, D.; Weinhardt, C.
2021. Proceedings of the 54th Hawaii International Conference on System Sciences, University of Hawaii. doi:10.24251/HICSS.2021.401
Forecasting Energy Technology Diffusion in Space and Time: Model Design, Parameter Choice and Calibration.
Heymann, F.; Vom Scheidt, F.; Soares, F. J.; Duenas, P.; Miranda, V.
2021. IEEE Transactions on Sustainable Energy, 12 (2), 802–809. doi:10.1109/TSTE.2020.3020426
Vehicle Scheduling and refueling of Hydrogen Buses with On-site Electrolysis.
Golla, A.; Scheidt, F. vom; Röhrig, N.; Staudt, P.; Weinhardt, C.
2021. Informatik 2020 - Back to the future. Hrsg.: R. H. Reussner, 795–806, Gesellschaft für Informatik e.V.  (GI). doi:10.18420/inf2020_70
The Efficiency and Distributional Effects of Alternative Residential Electricity Rate Designs.
Burger, S. P.; Knittel, C. R.; Perez-Arriaga, I. J.; Schneider, I.; vom Scheidt, F.
2020. The energy journal, 41 (1), 199–239. doi:10.5547/01956574.41.1.sbur
The German Electricity System in 2030: Data on Consumption, Generation, and the Grid.
vom Scheidt, F.; Müller, C.; Staudt, P.; Weinhardt, C.
2020, November 3. doi:10.5445/IR/1000125576
The German electricity system in 2030: data on consumption, generation, and the grid.
vom Scheidt, F.; Müller, C.; Staudt, P.; Weinhardt, C.
2020, Oktober 8. doi:10.5445/IR/1000124167
Data analytics in the electricity sector – A quantitative and qualitative literature review.
Scheidt, F. vom; Medinová, H.; Ludwig, N.; Richter, B.; Staudt, P.; Weinhardt, C.
2020. Energy and AI, 1, Article no: 100009. doi:10.1016/j.egyai.2020.100009
The Efficiency and Distributional Effects of Alternative Residential Electricity Rate Designs.
Burger, S. P.; Knittel, C. R.; Pérez-Arriaga, I. J.; Schneider, I.; vom Scheidt, F.
2019. National Bureau of Economic Research. doi:10.3386/w25570
Smart Peak Shaving für Industriekunden – Lastspitzen-Vorhersage mit Maschinellem Lernen.
vom Scheidt, F.; Becker, J.; Huber, J.
2019. FZI Open Innovation (2019), Karlsruhe, Deutschland, 16. Oktober 2019
DER Adopter Analysis using Spatial Autocorrelation and Information Gain Ratio under different Census-data Aggregation Levels.
Heymann, F.; Lopes, M.; vom Scheidt, F.; Silva, J. M.; Duenas Martinez, P.; Soares, F.; Miranda, V.
2019. IET Renewable power generation. doi:10.1049/iet-rpg.2019.0322
Assessing the Economics of Residential Electricity Tariff Selection.
Vom Scheidt, F.; Staudt, P.; Weinhardt, C.
2019. Proceedings of the 2nd International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 9 - 11 September 2019, Article No.8849143, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SEST.2019.8849143
Behavioral Studies In Energy Economics: A Review And Research Framework.
Staudt, P.; Golla, A.; Richter, B.; Schmidt, M.; Scheidt, F. vom; Weinhardt, C.
2019. 42nd IAEE International Conference: Local Energy, Global Markets (IAEE 2019), Montreal, CDN, May 29 - June 1, 2019
Towards Smart Distribution Grids: A Structured Market Engineering Review.
Dauer, D.; Scheidt, F. vom; Weinhardt, C.
2017. Proceedings of the Second KSS Research Workshop : Karlsruhe, Germany, February 2016. Ed.: P. Hottum, 47–58, Karlsruher Institut für Technologie (KIT)
Towards Smart Distribution Grids: A Structured Market Engineering Review.
Dauer, D.; Scheidt, F. vom; Weinhardt, C.
2016. Proceeings of the Second Karlsruhe Service Summit Research Workshop, Karlsruhe, 25. - 26. Februar 2016

Betreute Masterarbeiten

Data Analytics in the Electricity Sector – A Data Mining driven Review Probabilistic

Forecasting of Individual Electrical Load Using GRU Considering Weather Effects

Optimal Selection of Time-varying Electricity Tariffs – A Multi-Classification Problem

Industry peak load forecasting with machine learning

Reduktion von Spitzenlasten in Niederspannungsnetzen mit aggregierten Haushaltsbatterien - Effekte von Vorhersagemethoden und Netzentgelten

Klassifikation von Haushaltslastkurven hinsichtlich dezentraler Energietechnologien und Stromtarifen unter Verwendung einer LSTM Architektur

Optimization and Machine Learning for Energy Services

Cost-minimal Siting and Sizing of Domestic Power-to-Gas plants for Hydrogen Fueled Transportation: The impact of electricity tariffs