Tobias Gebhard

M.Sc. Tobias Gebhard

Residential Power Demand Modeling

S3|10 310
Landgraf-Georg-Str. 4
64283 Darmstadt

Research Interest

  • Modeling of Residential Energy Demand
  • Modeling of Consumer Dependencies/Synchronization
  • Probabilistic Modeling
  • Data Analytics
  • Digital Twins


The modeling of energy demand is important for predictions and ensuring power grid stability. The residential sector exhibits much uncertainty created by human behavior and increasing electricity demand, e.g. due to more and more heat pumps (HPs) and electric vehicles (EVs). In demand modeling, consumers are often considered independent from each other because peaks at different times mostly balance out. However, the synchronized change of power demands can quickly pose a problem to power grids (also known as the "TV pick-up" effect). This could potentially lead to blackouts, as distribution grids are not designed for every household to receive maximal power simultaneously. Therefore, a better understanding of consumer dependencies and the early detection of such anomalies is necessary.

These ideas are investigated in an interdisciplinary context together with the research center emergenCITY, funded by the LOEWE initiative (Hesse, Germany) and the DLR, Institute for the Protection of Terrestrial Infrastructures. Based on the digital city of the future, we tackle the questions on how to maintain its functionalities even in extreme situations, crises and disasters and to increase the reliability and resilience of critical infrastructures. We build demonstrators that simulate a city (for example Darmstadt) as a socio-technical system, considering the interdependencies of energy, communication and water infrastructure.



Open theses

Supervisor: Tobias Gebhard
Earliest start: immediately
Type: Master Thesis

Residential electricity consumption represents a significant part of the overall electricity demand. In order to forecast the demand, accurate models that describe the consumption of households are needed. While deterministic models are useful for longer periods (e.g. days, years), consumption patterns seem to get stochastic at shorter timescales. If not just one household but the population of a city is considered, the aggregated demand is of interest. It usually stays within a narrow band because consumers are mostly independent from each other. However, crises or other events (e.g. the TV program [3]) can cause a synchronization of people, which can lead to critically high electricity use. Synchronization can be induced by external factors (like the daily cycle or weather influences) or internal effects emerging from the people (e.g. panic behavior). A general model to describe the synchronization of consumers has been developed in [1].

In the thesis, this model shall be tested/validated with measured consumption data by using statistical or machine learning methods. The dataset [2] contains the electric load of 38 households in 10 seconds resolution over more than 2 years. With this, detailed load profiles of the individual as well as the aggregated demand can be constructed, while being able to distinguish between season, weekday, and time of the day. It is to be investigated whether the synchronization model [1] can be reasonably applied to the data. As the dataset also contains weather data, the representation of known/predicted synchronization from external factors could be examined.



  • Basic Knowledge in (multivariate) statistics
  • Attendance in lecture “Machine Learning & Energy” helpful
  • Interest in working with data

[1] Tobias Gebhard; Eva Brucherseifer; Florian Steinke:  Monitoring Electricity Demand Synchronization Using Copulas.

[2] Schlemminger et. Al.: Dataset on electrical single-family house and heat pump load profiles in Germany



Short Bio

  • Since 2021: PhD Student at EINS and researcher at DLR
  • 2018 - 2021: M.Sc. Mechatronik at TU Darmstadt
  • 2014 - 2018: B.Sc. Mechatronik at TU Darmstadt


Monitoring Electricity Demand Synchronization Using Copulas

Tobias Gebhard; Eva Brucherseifer; Florian Steinke :
Monitoring Electricity Demand Synchronization Using Copulas.
In: IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Novi Sad, Serbia, 2022