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


Flexibility Potential of Future EV-Fleets in Germany
Signer, T.; Nefferdorf, J.; Kleinebrahm, M.; Stumpf, S.; Fichtner, W.
2025, Mai 17. 2nd International Electric Vehicle Conference (2025), Stuttgart, Deutschland, 14.–16. Mai 2025
Countries across the world use more land for golf courses than wind or solar energy
Weinand, J. M.; Pelser, T.; Kleinebrahm, M.; Stolten, D.
2025. Environmental Research Communications, 7 (2), Art.-Nr.: 021012. doi:10.1088/2515-7620/adb7bd
Household heat and electricity consumption data from three living lab interventions in the Energy Smart Home Lab
Sandmeier, T.; Scharnhorst, L.; Kleinebrahm, M.; Fichtner, W.
2025. doi:10.35097/txjj7yawp9gr4ts8
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
Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps
Dengiz, T.; Kleinebrahm, M.
2024. arxiv. doi:10.48550/arXiv.2407.11561
Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles
Dengiz, T.; Raith, A.; Kleinebrahm, M.; Vogl, J.; Fichtner, W.
2024. arxiv. doi:10.48550/arXiv.2407.11719
Input and result data for the paper "Imitation learning with artificial neural networks for demand response with a heuristic control approach for heat pumps"
Dengiz, T.; Kleinebrahm, M.
2024, Juni 28. doi:10.35097/JieKFMOeZgzCYGmh
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
Input data and result data for the paper "Pareto local search for a multi-objective demand response problem in residential areas with heat pumps and electric vehicles"
Dengiz, T.; Kleinebrahm, M.
2024, Juli 1. doi:10.35097/ZxeqNfKvVlQcjQAt
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
„Ein teurer Irrweg“ – KIT Studie zeigt: energieautarke Eigenheime sind keine Strategie für die Energiewende in Europa - Campus-Report am 26.12.2023
Fuchs, S.; Kleinebrahm, M.
2023. doi:10.5445/IR/1000166059
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
Corrigendum to “Assessing the contribution of simultaneous heat and power generation from geothermal plants in off-grid municipalities” [Appl. Energy 255 (2019) 113824]
Weinand, J. M.; McKenna, R.; Kleinebrahm, M.; Mainzer, K.
2022. Applied Energy, 307, Art.-Nr.: 118202. doi:10.1016/j.apenergy.2021.118202
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
Assessing the contribution of simultaneous heat and power generation from geothermal plants in off-grid municipalities
Weinand, J. M.; McKenna, R.; Kleinebrahm, M.; Mainzer, K.
2019. Applied energy, 255, 113824. doi:10.1016/j.apenergy.2019.113824
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/