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28 Research Topics
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71 Application Fields
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36 Institutions
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Energy and comfort optimization in living and working environments through a user-centered predictive control

Lay summary

User need for comfort is one of the most critical barriers for energy efficient living and working. Recent advancements in auto-matic optimization of room climate successfully foster energy efficiency, but usually ignore comfort needs. In this project, we will introduce an approach which aims at maximizing energy efficiency and user comfort at the same time. For this purpose, Empa NEST is used as a testbed. Machine learning based state of the art energy optimization algorithms (Reinforcement Learning) are combined with real time user feedback. Our previous work suggests an energy saving potential of around 20%.

Last updated:18.06.2022

  Bratislav Svetozarevic