Dr.-Ing. Max Kleinebrahm Max Kleinebrahm

Dr.-Ing. Max Kleinebrahm

Activities

Research interests

  • Analysis and modeling of decentralized energy systems
  • Energy self-sufficient residential buildings
    • Methods: optimization, clustering
  • Modeling of resident behavior (activity and mobility patterns)
  • Generation of synthetic data (sequences, time series)
    • Methods: neural networks (Transformer, GAN, LSTM, MDN, TCN), Markov chains, differential privacy

Projects

Teaching

  • Supervision of energy economics seminars
  • Supervision of Bachelor and Master theses (have a look here)

Profile

  • Head of research group "Energy Demand & Mobility" since October 2023
  • Research assistant at the Chair of Energy Economics since March 2017
  • Bachelor's and master's degree in industrial engineering specializing in mechanical engineering / energy technology at RWTH Aachen University and the Norwegian University of Science and Technology (NTNU)
  • Experience as an intern at Drees & Sommer Advanced Building Technologies GmbH, German University of Technology (GUTech) in Oman and as a student assistant at the Fraunhofer Institute for Production Technology (IPT) at RWTH Aachen University
  • Research stay at the University of Reading as a CREDS Visitor

Publications


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, June 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. PhD dissertation
Kleinebrahm, M.
2024, May 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, July 1. doi:10.35097/ZxeqNfKvVlQcjQAt
Electric Mobility in PowerACE
Signer, T.; Sandmeier, T.; Weiskopf, T.; Kleinebrahm, M.; Fichtner, W.
2024, March 21. Agent-Based Modeling for Energy Economics and Energy Policy (ABM4Energy 2024), Freiburg, Germany, March 21–22, 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, March 21. Agent-Based Modeling for Energy Economics and Energy Policy (ABM4Energy 2024), Freiburg, Germany, March 21–22, 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, Germany, June 12–13, 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, April 19–30, 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, June 26. 30th European Conference on Operational Research (EURO 2019), Dublin, Ireland, June 23–26, 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

Conferences

Conference/workshop 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.

 

Presentations:

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/