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- W4285822591 abstract "With increased complexity due to time-variable renewable electricity supply and associated variable cost, it has become evident that management of building energy demand as a resource is essential for grid stabilization. However, techno-economic and human constraints make such solutions non-trivial. Reinforcement learning (RL), a discipline of machine learning, was explored to take on this challenge. The open-source Functional Mock-up Interface - Machine Learning Center (FMI-MLC) was developed to provide a standardized interface of RL and simulation environment, through the FMI industry standard for co-simulation. RL demonstrated its ability to operate an electrochromic window and heating, ventilation, and air conditioning system (HVAC), and reduce demand during critical periods of the electric power grid." @default.
- W4285822591 created "2022-07-19" @default.
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- W4285822591 date "2021-09-01" @default.
- W4285822591 modified "2023-10-02" @default.
- W4285822591 title "Advanced Control of Dynamic Facades and HVAC with Reinforcement Learning based on standardized co-Simulation" @default.
- W4285822591 doi "https://doi.org/10.26868/25222708.2021.30432" @default.
- W4285822591 hasPublicationYear "2021" @default.
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