Dr.-Ing.  Max Kleinebrahm Max Kleinebrahm

Dr.-Ing. Max Kleinebrahm

Tätigkeitsbereich

Forschungsinteressen

  • Analyse und Modellierung dezentraler Energieversorgungssysteme 
  • Energieautarke Gebäude
  • Erneuerbare Energien
    • Methoden: Optimierung (MILPs), Clustern
  • Modellierung von Bewohnerverhalten (Aktivitäts- und Mobilitätsmuster)
  • Generierung synthetischer Daten (Sequenzen)
    • Methoden: Neronale Netze (Transformers, GANs, LSTM, MDNs, TCNs), Markov Ketten, Differential Privacy

Projekte

Lehre

  • Betreuung von energiewirtschaftlichen Seminaren
  • Betreuung von Bachelor- und Masterarbeiten

Profil

  • Leiter der Forschungsgruppe "Energienachfrage und Mobilität" seit Oktober 2023
  • Wissenschaftlicher Mitarbeiter am Lehrstuhl für Energiewirtschaft seit März 2017
  • Bachelor- und Masterstudiengang Wirtschaftsingenieurwesen mit Schwerpunkt Maschinenbau / Energietechnik an der RWTH Aachen und der Norwegischen Universität für Wissenschaft und Technologie (NTNU)
  • Erfahrung als Praktikant bei der Drees & Sommer Advanced Building Technologies GmbH der Deutschen Technischen Universität (GUTech) im Oman und als studentische Hilfskraft am Fraunhofer-Institut für Produktionstechnik (IPT) der RWTH Aachen
  • Forschungsaufenthalt an der University of Reading als CREDS-Besucher

Veröffentlichungen


Impacts of climate change on the European electricity market
Weiskopf, T.; Jahnke, E.; Kleinebrahm, M.; Britto, A.
2024. 2024 20th International Conference on the European Energy Market (EEM), Istanbul, Turkiye, 10-12 June 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/EEM60825.2024.10608934
Techno-Economic Analysis of Future Process-Specific Demand Response in European Industries
Scharnhorst, L.; Xie, X.; Kleinebrahm, M.; Fichtner, W.
2024. 20th International Conference on the European Energy Market (EEM 2024), 10 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/EEM60825.2024.10608488
Scaling energy system optimizations: Techno-economic assessment of energy autonomy in 11 000 German municipalities
Risch, S.; Weinand, J. M.; Schulze, K.; Vartak, S.; Kleinebrahm, M.; Pflugradt, N.; Kullmann, F.; Kotzur, L.; McKenna, R.; Stolten, D.
2024. Energy Conversion and Management, 309, Art.-Nr.: 118422. doi:10.1016/j.enconman.2024.118422
Future Residential Energy System Design. Dissertation
Kleinebrahm, M.
2024, Mai 28. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000170239
Electric Mobility in PowerACE
Signer, T.; Sandmeier, T.; Weiskopf, T.; Kleinebrahm, M.; Fichtner, W.
2024, März 21. Agent-Based Modeling for Energy Economics and Energy Policy (ABM4Energy 2024), Freiburg im Breisgau, Deutschland, 21.–22. März 2024
Integrating flexible demand in the sectors industry and households into the agent-based electricity market model PowerACE
Scharnhorst, L.; Schuhmacher, J.; Jahnke, E.; Signer, T.; Kleinebrahm, M.; Ardone, A.; Fichtner, W.
2024, März 21. Agent-Based Modeling for Energy Economics and Energy Policy (ABM4Energy 2024), Freiburg im Breisgau, Deutschland, 21.–22. März 2024
Two million European single-family homes could abandon the grid by 2050
Kleinebrahm, M.; Weinand, J. M.; Naber, E.; McKenna, R.; Ardone, A.; Fichtner, W.
2023. Joule, 7 (11), 2485–2510. doi:10.1016/j.joule.2023.09.012
Disruptive Events within the RESUR Project: Identification and Modeling – Helmholtz platform for the design of robust energy systems and their supply chains
Dickler, S.; Ardone, A.; Poganietz, W.-R.; Ross, A.; Weinand, J.; Zapp, P.; Shamon, H.; Rösch, C.; Haase, M.; Kraft, E.; Kebrich, S.; Kullmann, F.; Kleinebrahm, M.; Hoffmann, J.; Vögele, S.; Goerge, M.
2023. Helmholtz Energy Conference (2023), Koblenz, Deutschland, 12.–13. Juni 2023
Multivariate time series imputation for energy data using neural networks
Bülte, C.; Kleinebrahm, M.; Yilmaz, H. Ü.; Gómez-Romero, J.
2023. Energy and AI, 13, Artikl.Nr.: 100239. doi:10.1016/j.egyai.2023.100239
Analysing municipal energy system transformations in line with national greenhouse gas reduction strategies
Kleinebrahm, M.; Weinand, J. M.; Naber, E.; McKenna, R.; Ardone, A.
2023. Applied Energy, 332, Art.-Nr.: 120515. doi:10.1016/j.apenergy.2022.120515
Dissemination of PV-Battery systems in the German residential sector up to 2050: Technological diffusion from multidisciplinary perspectives
Vogele, S.; Poganietz, W.-R.; Kleinebrahm, M.; Weimer-Jehle, W.; Bernhard, J.; Kuckshinrichs, W.; Weiss, A.
2022. Energy, 248, Artk.Nr.: 123477. doi:10.1016/j.energy.2022.123477
Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law
Braeuer, F.; Kleinebrahm, M.; Naber, E.; Scheller, F.; McKenna, R.
2022. Applied Energy, 305, Art.-Nr.: 117884. doi:10.1016/j.apenergy.2021.117884
The impact of public acceptance on cost efficiency and environmental sustainability in decentralized energy systems
Weinand, J. M.; McKenna, R.; Kleinebrahm, M.; Scheller, F.; Fichtner, W.
2021. Patterns, 2 (7), Art.-Nr.: 100301. doi:10.1016/j.patter.2021.100301
Research trends in combinatorial optimization
Weinand, J. M.; Sörensen, K.; San Segundo, P.; Kleinebrahm, M.; McKenna, R.
2022. International transactions in operational research, 29 (2), 667–705. doi:10.1111/itor.12996
Development of a dynamic European residential building stock typology for energy system analysis
Kleinebrahm, M.; Naber, E.; Weinand, J.; McKenna, R.; Ardone, A.
2021, April 19. European Geosciences Union General Assembly (EGU 2021), Online, 19.–30. April 2021. doi:10.5194/egusphere-egu21-8001
Using neural networks to model long-term dependencies in occupancy behavior
Kleinebrahm, M.; Torriti, J.; McKenna, R.; Ardone, A.; Fichtner, W.
2021. Energy and buildings, 240, Art.Nr. 110879. doi:10.1016/j.enbuild.2021.110879
Identification of Potential Off-Grid Municipalities with 100% Renewable Energy Supply for Future Design of Power Grids
Weinand, J. M.; Ried, S.; Kleinebrahm, M.; McKenna, R.; Fichtner, W.
2022. IEEE transactions on power systems, 37 (4), 3321–3330. doi:10.1109/TPWRS.2020.3033747
Using neural networks to model long-term dependencies in occupancy behavior
Kleinebrahm, M.; Torriti, J.; McKenna, R.; Ardone, A.; Fichtner, W.
2020. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000126271
Identification of potential off-grid municipalities with 100% renewable energy supply
Weinand, J. M.; Ried, S.; Kleinebrahm, M.; McKenna, R.; Fichtner, W.
2020. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000118013
Developing a combinatorial optimisation approach to design geothermal-based district heating systems
Weinand, J. M.; Kleinebrahm, M.; McKenna, R.; Mainzer, K.; Fichtner, W.
2019, Juni 26. 30th European Conference on Operational Research (EURO 2019), Dublin, Irland, 23.–26. Juni 2019
Effects of the tenants electricity law on energy system layout and landlord-tenant relationship in a multi-family building in Germany
Braeuer, F.; Kleinebrahm, M.; Naber, E.
2019. IOP conference series / Earth and environmental science, 323, Art.-Nr.: 012168. doi:10.1088/1755-1315/323/1/012168
Developing a combinatorial optimisation approach to design district heating networks based on deep geothermal energy
Weinand, J. M.; Kleinebrahm, M.; McKenna, R.; Mainzer, K.; Fichtner, W.
2019. Applied energy, 251, 113367. doi:10.1016/j.apenergy.2019.113367
Developing a three-stage heuristic to design geothermal-based district heating systems
Weinand, J.; Kleinebrahm, M.; McKenna, R.; Mainzer, K.; Fichtner, W.
2019. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000090290
Optimal Renewable Energy Based Supply Systems for Self-sufficient Residential Buildings
Kleinebrahm, M.; Weinand, J.; Ardone, A.; McKenna, R.
2018. BauSIM2018 - 7. Deutsch-Österreichische IBPSA-Konferenz : Tagungsband. Hrsg.: P. von Both, 164–171, Karlsruher Institut für Technologie (KIT)
A stochastic multi-energy simulation model for UK residential buildings
McKenna, R.; Hofmann, L.; Kleinebrahm, M.; Fichtner, W.
2018. Energy and buildings, 168, 470–489. doi:10.1016/j.enbuild.2018.02.051

Konferenzbeiträge

Schriftliche Beiträge / Proceedings:

Exploring socioeconomic and temporal characteristics of British and German residential energy demand. McKenna, R.; Kleinebrahm, M.; Yunusov, T.; Lorincz, M.; Torriti, J. British Institute of Energy Economics 2018, 18-19 September 2018, Oxford, UK.

Using attention to model long-term dependencies in occupancy behavior. Kleinebrahm, M.; Torriti, J.; McKenna, R.; Ardone, A.; Fichtner, W. Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.

Vorträge:

Weinand, J.; Kleinebrahm, M.; Mainzer, K.; McKenna, R. (2018): Exploring the technical and economic feasibility of complete municipal energy autonomy: A case study for Germany, 41st IAEE International Conference, 10-13 June, Groningen, Netherlands.

Kachirayil, F.; McKenna, R.; Weinand, J.; Kleinebrahm, M.; Huckebrink, D.; Bertsch, V. (2021): Quantifying the trade-off between cost and security of supply for 100% renewable local energy systems. ProMETS Workshop: Prospektive multidimensionale Bewertung von Energietechnologien und -​szenarien, 25-26 Feburary, Oldenburg, Germany

Kleinebrahm, M., Naber, E., Weinand, J., McKenna, R., and Ardone, A.: Development of a dynamic European residential building stock typology for energy system analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8001, https://doi.org/10.5194/egusphere-egu21-8001,  2021.

Weinand, J., McKenna, R., Kleinebrahm, M., and Scheller, F.: Quantifying the trade-off between public acceptance and cost efficiency in decentralized energy systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-716, https://doi.org/10.5194/egusphere-egu21-716,  2021.

Kleinebrahm, M. (2021): Synthetic data for a better understanding of residential energy demand - Synthetic Data Meetup Webinar - https://mostly.ai/2021/04/27/synthetic-data-for-residential-energy/