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- W4386432462 abstract "This paper explores the feasibility and strategies of using model-free reinforcement learning-based control (RLC) for the slow response radiant floor heating (RFH) systems with a setback setting. First, a detailed physics-based virtual testbed is developed and validated. Then based on the virtual testbed, four different strategies of RLC to handle the slow response are studied, along with a conventional rule-based control (RBC) without setback as a baseline and an MPC with a setback for the upper bound on the performance. The results show that the DQN_TD(λ) with forecasted weather data as states provides the best performance, showing potential for applications. Compared to the baseline, the heating demand is reduced by 19.1% with RLC and 18.5% with MPC. The unmet hours of RLC with our settings are higher than that of MPC, which suggests that more research is needed for RLC to better meet the constraints." @default.
- W4386432462 created "2023-09-05" @default.
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- W4386432462 date "2023-01-01" @default.
- W4386432462 modified "2023-10-07" @default.
- W4386432462 title "Model-Free Reinforcement Learning-Based Control for Radiant Floor Heating Systems" @default.
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- W4386432462 doi "https://doi.org/10.1007/978-981-19-9822-5_150" @default.
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