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  4. Energy Optimal Control of a Multivalent Building Energy System using Machine Learning
 
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2021
  • Konferenzbeitrag

Titel

Energy Optimal Control of a Multivalent Building Energy System using Machine Learning

Abstract
In this contribution we develop and analyse intelligent control methods in order to optimise the energy efficiency of a modern residential building with multiple renewable energy sources. Because of alternative energy production options a non-convex mixed-integer optimisation problem arises. For the solution we first apply combined optimisation methods and integrate it into a model predictive controller. In comparison, a reinforcement learning based approach is developed and evaluated in detail. Both methods, in particular reinforcement learning approaches are able to decrease energy consumption and keep thermal comfort at the same time.
Author(s)
Huang, Chenzi
Fraunhofer-Institut fĂ¼r Integrierte Schaltungen IIS
Seidel, Stephan
Fraunhofer-Institut fĂ¼r Integrierte Schaltungen IIS
Jia, Xuehua
Fraunhofer-Institut fĂ¼r Integrierte Schaltungen IIS
Paschke, Fabian
Fraunhofer-Institut fĂ¼r Integrierte Schaltungen IIS
Bräunig, Jan
Fraunhofer-Institut fĂ¼r Integrierte Schaltungen IIS
Hauptwerk
10th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2021. Proceedings
Project(s)
ARCHE
Funder
Bundesministerium fĂ¼r Wirtschaft und Energie BMWi (Deutschland)
Konferenz
International Conference on Smart Cities and Green ICT Systems (SMARTGREENS) 2021
International Conference on Enterprise Information Systems (ICEIS) 2021
International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) 2021
International Conference on Cloud Computing and Services Science (CLOSER) 2021
DOI
10.5220/0010478500570066
File(s)
N-634736.pdf (1.21 MB)
Language
Englisch
google-scholar
EAS
Tags
  • reinforcement learnin...

  • Model Predictive Cont...

  • Building Energy Syste...

  • machine learning

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