Schlüter, Stephan, Prof. Dr.

Stephan Schlüter

Professor Dr.

Institut für Energie und Antriebstechnik Institut für Angewandte Forschung


Fields of Interest:
  • Multivariate time series analysis
  • Statistical analysis and modelling of energy commodities
  • Time series forecasting
  • Renewable energies
  • Environmental Statistics
  • Big Data

Currently I'm a Co-Editor of the Journal Spatial Information Research.


  • Höhere Mathematik
  • Operations Research
  • Statistik

Ich habe immer Themen für Studien-, Bachelor- oder Masterarbeiten. Bitte wenden Sie sich diesbezüglich per eMail oder persönlich an mich.

Publications and Paper

Hwang Y-S, Roh J W, Suh D, Otto M-O, Schlüter S, Choudhurry T, Huh J-S. No Evidence for Global Decrease in CO2 Concentration During the First Wave of COVID‑19 Pandemic. Environmental Monitoring and Assessment, 2021, 193:751.

von Döllen A, Hwang Y, Schlüter S. The Future Is Colorful—An Analysis of the CO2 Bow Wave and Why Green Hydrogen Cannot Do It Alone. Energies 2021, 14, 5720.

Hwang Y, Schlüter S, Choudhury, T, Um J-S. Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries. Sustainability 2021.

Liebermann S, Um J-S, Hwang Y, Schlüter, S. Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts. Energies 2021, 14, 3030. DOI:

Hwang Y, Um J-S,  Hwang J, Schlüter S. Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux. Energies 2020. .

Kreuzer D, Schlüter S, Munz M. Short-term temperature forecasts using a convolutional neural network — An application to different weather stations in Germany. Machine Learning with Applications 2020, 2,

Hwang Y, Um J-S, Schlüter S. Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables. Int. J. Environ. Res. Public Health 2020, 17, 5976.

Schlüter S, Kresoja M. Two Preprocessing Algorithms for Climate Time Series. Journal of Applied Statistics 2019,
Schlüter S. A sine-based Model for the Volatility of Daily Photovoltaic Production. Working Paper, 2017.

Hanfeld M, Schlüter S. Operating a Swing Option on Today's Gas Markets - How Least Squares Monte Carlo Works and Why it is Beneficial. Zeitschrift für Energiewirtschaft, 2:137-145, 2017.

Herwartz H, Schlüter S. On the Predictive Information of Futures' Price - a Wavelet Based Assessment. Journal of Forecasting, 2016.

Schlüter S, Deuschle C. Wavelet-Based Forecasting of ARIMA Time Series - an Empirical Comparison of Different Methods. Managerial Economics, 15(1):107-131, 2015.

Schlüter S, Hanfeld M. Pricing Asian Oil Options using Polynomial Quantile Functions. IEEE Conference Proceedings of the EEM 2014, Krakow, 2014.

Schlüter S, Fischer M. A Tail Quantile Approximation for the Student T Distribution. Communications in Statistics: Theory and Methods, 41(15):2617-2625, 2012.

Schlüter S, Fischer M. The Weak Tail Dependence Coefficient of the Elliptical Generalized Hyperbolic Distribution. Extremes,  04 2011.

Schlüter S. A Long-Term/Short-Term Model for Daily Electricity Prices with Dynamic Volatility. Energy Economics, 32(5):1074-1081, 2010.

Fischer M, Köck C, Schlüter S, Weigert F. An Empirical Analysis of Multivariate Copula Models. Quantitative Finance, 9(7):839-854, 2009.

Prof. Dr. Stephan Schlüter
Raum: B207
Prittwitzstraße 10
89075 Ulm
Fon: +49 (0)731 50-28265