1. Proceedings
  2. I3M
  3. 2021
  4. SESDE
  5. 10.46354/i3m.2021.sesde.007

Optimization of Complex Thermally Electrically Coupled Buildings using Genetic Programming to Identify Optimal Energy Flow Controllers

  • a Kathrin Kefer , 
  • b Patrick Kefer, 
  • c Roland Hanghofer,
  • d Markus Stöger,
  • e Bernd Hofer,
  • f Michael Affenzeller ,
  • g Stephan Winkler 
  • a,d,e Research & Development FH OÖ Forschungs und Entwicklungs GmbH: Research Group Heuristic and Evolutionary
    Algorithms Laboratory, Softwarepark 11, Hagenberg, A-4232, Austria
  • b Research & Development FH OÖ Forschungs und Entwicklungs GmbH: Research Group ASIC, Ringstraße 43a, Wels, A-4600, Austria
  • f.g Research & Development FH OÖ Forschungs und Entwicklungs GmbH: Research Group Heuristic and Evolutionary
    Algorithms Laboratory, Softwarepark 11, Hagenberg, A-4232, Austria 
  • c 4Dynatrace Austria GmbH, Softwarepark 32, Hagenberg, A-4232, Austria
Cite as
Kefer K., Kefer P., Hanghofer R., Stöger M., Hofer B., Affenzeller M., Winkler S. (2021). Optimization of Complex Thermally Electrically Coupled Buildings using Genetic Programming to Identify Optimal Energy Flow Controllers. Proceedings of the 9th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2021), pp. 55-65. DOI: https://doi.org/10.46354/i3m.2021.sesde.007

Abstract

During the last years, renewable energy sources and their management have become increasingly important to help driving forward the energy transition and slow down the global warming. Current energy management systems are either simple but not optimal or very complex, computationally intensive and optimal. Despite that, they also often focus on the optimization of just the electrical energy ows of buildings so far. This work focuses on the development of a linear model predictive controller as well as heuristic energy ow controllers for optimizing a complex thermally-electrically coupled system. For that, a real
world building is modelled in MATLAB Simulink and used for the training process of the heuristic controllers as well as for the evaluation of the different optimizers in simulation with different timespans. It is found that the linear MPC works better than a rule-based self consumption optimization and that the heuristic controllers work significantly better than these two for all evaluation timespans up to 180 days, while they perform significantly worse for 364 days.

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