Welcome to the homepage of the Research group of Applied Mathematics for Energy Markets, where innovation meets sustainability in response to the undeniable reality of climate change. As this global phenomenon impacts various facets of our daily lives, the energy industry finds itself on the frontlines, facing financial consequences felt by both large enterprises and small companies.
Our research group is actively engaged in exploring advanced mathematical methods to address the pressing issues of today's energy landscape. Through rigorous research, we aim to provide actionable solutions that not only alleviate the immediate pressures faced by the industry but also pave the way for a resilient and environmentally conscious energy future.
Join us on this journey of exploration and discovery, where mathematics becomes a powerful tool in shaping the energy economics of tomorrow. Together, we strive to make a meaningful impact on the intersection of mathematics, energy, and the global fight against climate change.
These research fields represent our commitment to pushing the boundaries of knowledge and contributing to the ongoing dialogue on sustainable energy practices. Explore our findings and delve into the cutting-edge advancements that fuel our mission for a more resilient and environmentally conscious energy future.
Members
Prof. Dr. Stephan Schlüter
Abhinav Das, M.Sc.
Mr. Erick Michel Lara Pinal, M.Sc.
Milena Kojić, PhD
Petar Mitić, PhD
Dr. Yong Seok Hwang
Research
Publications
Mitić P, Kojić M, Hanić A, Schlüter S. Environment and Economy Interactions in the Western Balkans: Current Situation and Prospects (2023). In: Tufek-Memišević, T., Arslanagić-Kalajdžić, M., Ademović, N. (eds) Interdisciplinary Advances in Sustainable Development. ICSD 2022. Lecture Notes in Networks and Systems, vol 529. Springer, Cham. https://doi.org/10.1007/978-3-031-17767-5_1
Hwang Y-S, Schlüter S, Um J-S. Cross-correlation of GOSAT CO2 Concentration with Repeated Heat-Wave-induced Photosynthetic Inhibition in Europe from 2009 to 2017. Remote Sensing, 2022. URL: https://www.mdpi.com/2072-4292/14/18/4536/pdf
Das, A., Makogin, V., & Spodarev, E. (2022). Extrapolation of stationary random fields via level sets. Theory of Probability and Mathematical Statistics, 106, 85-103.
Schlüter S, Jung S, von Döllen A, Lee W. An Alternative to Index-Based Gas Sourcing Using Neural Networks. Energies, 2022, 15, 4708. https://www.mdpi.com/1996-1073/15/13/4708/pdf .
Kojić M, Schlüter S, Mitić P, Hanić A. Economy-environment nexus in developed European countries: Evidence from multifractal and wavelet analysis. Chaos, Solitons and Fractals, 2022, 160, 112189.
Schlüter S, Menz F, Kojić M, Mitić P, Hanić A. A Novel Approach to Generate Hourly Photovoltaic Power Scenarios. Sustainability, 2022, 14, 4617. https:// doi.org/10.3390/su14084617.
Hwang Y-S, Schlüter S, Park S-I, Um J-S. Comparative Evaluation for Tracking the Capability of Solar Cell Malfunction Caused by Soil Debris between UAV Video versos Photo-Mosaic. Remote Sensing, 2022, 14, 1220.
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: https://doi.org/10.3390/en14113030.
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. https://www.mdpi.com/1996-1073/13/22/6009/pdf .
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, https://doi.org/10.1016/j.mlwa.2020.100007.
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, https://doi.org/10.1080/02664763.2019.1701637.
Schlüter S. A sine-based Model for the Volatility of Daily Photovoltaic Production. Working Paper, 2017.
Opportunities
Our group is actively looking for students and researchers who can contribute in the field of Energy Markets. We generally work with mathematical modeling of the Energy Market hence we prefer to work with students/researchers having background in Probability, Statistics, Mathematical Finance, Econometrics, Machine Learning (at least one of these subjects). Bachelor and Masters students having the background in aforementioned area are welcome for their thesis. The topics for bachelor and master thesis can be mutually decided.
