Mario Beykirch

M.Sc. Mario Beykirch

Stochastic optimization

+49 (0) 6151 16-21716
fax +49 (0) 6151 16-21712

S3|10 303
Landgraf-Georg-Str. 4
64283 Darmstadt

Research Interest

To be done

Open theses

Supervisor: Mario Beykirch
Earliest start: immediately
Type: Bachelor Theses


To generate reliable forecasts of the thermal load of a building it is important to understand the dynamics that effect its heat load. Unfortunately, it is mostly not feasible to simulate an existing building in detail since this would require extensive knowledge about actual building parameters such as thermal transmittance of all the building parts. However, a building can be abstracted as a simple closed thermodynamic system that exchanges heat with the environment e.g. in the winter it loses heat to the environment, while gaining heat from the heat grid and solar radiation. This model would only have few unknown parameters which could be inferred from measured heat load and weather data.


The goal of this Bachelor Thesis is to learn a simple thermodynamic model of a building based on available data from the campus Lichtwiese using Bayesian inference methods. The data basis will be heating data of the last 3 years as well as estimated building parameters. This thesis will contribute to the research project “EnEff: Campus Lichtwiese” which aims to improve energy efficiency on the campus by creating a digital twin of the campus energy system.

Short Bio

  • Since 2018: PhD student at EINS
  • 2015 – 2018: M.Sc. Energy Science and Engineering at TU Darmstadt and École polytechnique fédérale de Lausanne
  • 2011– 2015: B.Sc. Physics at TU Darmstadt and University of Bristol

Publications

Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series

Mario Beykirch ; Tim Janke ; Imed Tayeche ; Florian Steinke :
*accepted* Probabilistic Forecast Combination for Anomaly Detection in Building Heat Load Time Series.
To appear in: 2021 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), virtual Conference, 18.-21.10.21,
[Conference Contribution] [TUDBiblio][BibTeX], (2021)

Multi-modales Echtzeit Energiemonitoring – Multi-modales echtzeit Energiemonitoring als Basis eines digitalen Zwillings des Energiesystems des Campus Lichtwiese der Technischen Universität Darmstadt

Christopher Ripp ; Mario Beykirch ; Johannes Oltmanns ; Florian Steinke :
Multi-modales Echtzeit Energiemonitoring – Multi-modales echtzeit Energiemonitoring als Basis eines digitalen Zwillings des Energiesystems des Campus Lichtwiese der Technischen Universität Darmstadt.
In: Digitalisieren – Sektoren koppeln – Flexibilisieren. Systemische Integration der Bioenergie und weiterer erneuerbarer Energien in Gebäuden & Quartieren, virtual Conference, 24.11.2020,
[Conference Contribution] [TUDBiblio][BibTeX], (2020)

Evaluation of Day-Ahead Electricity Price Predictions with Multi-Stage Stochastic Programs

Mario Beykirch ; Tim Janke ; Florian Steinke :
Evaluation of Day-Ahead Electricity Price Predictions with Multi-Stage Stochastic Programs.
In: 8th International Ruhr Energy Conference (INREC 2019), Essen, Germany, 25.-26.09.2019,
[Conference Contribution] [TUDBiblio][BibTeX], (2019)

Bayesian Inference with MILP Dispatch Models for the Probabilistic Prediction of Power Plant Dispatch

Mario Beykirch ; Tim Janke ; Florian Steinke :
Bayesian Inference with MILP Dispatch Models for the Probabilistic Prediction of Power Plant Dispatch.
In: 16th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, 18.09.2019 - 20.09.2019,
DOI: 10.1109/EEM.2019.8916530,
[Conference Contribution] [TUDBiblio][BibTeX], (2019)

Learning Dispatch Parameters of Thermal Power Plants from Observations

Mario Beykirch ; Tim Janke ; Florian Steinke :
Learning Dispatch Parameters of Thermal Power Plants from Observations.
In: 8th IEEE PES Innovative Smart Grid Technologies Conference Europe, Sarajevo, Bosnia-Herzegovina, 21.-25.10.2018,
DOI: 10.1109/ISGTEurope.2018.8571825,
[Conference Contribution] [TUDBiblio][BibTeX], (2018)