A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Model-Free Reinforcement Learning-Based Control for Radiant Floor Heating Systems
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.
Model-Free Reinforcement Learning-Based Control for Radiant Floor Heating Systems
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.
Model-Free Reinforcement Learning-Based Control for Radiant Floor Heating Systems
Environ Sci Eng
Wang, Liangzhu Leon (editor) / Ge, Hua (editor) / Zhai, Zhiqiang John (editor) / Qi, Dahai (editor) / Ouf, Mohamed (editor) / Sun, Chanjuan (editor) / Wang, Dengjia (editor) / Han, Xu (author) / Malkawi, Ali (author)
International Conference on Building Energy and Environment ; 2022
Proceedings of the 5th International Conference on Building Energy and Environment ; Chapter: 150 ; 1447-1455
2023-09-05
9 pages
Article/Chapter (Book)
Electronic Resource
English
| TIBKAT | 1999
Control of Multizone Hydronic Radiant Floor Heating Systems
| British Library Online Contents | 1994
Control of Multizone Hydronic Radiant Floor Heating Systems
| British Library Conference Proceedings | 1994