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About this paper
Yunfei Bai, Chenghao Li, Sunku Prasad Jenne, Hao Wang, Sizhe Zhang, Jihong Wang
Authors develop a physics-informed neural network (PINN) that estimates a room’s changing temperature field from recent changes in sensor readings, rather than elapsed time. In a three-hour simulated occupant-centric control test, estimates differed from COMSOL’s by an average relative error of 0.67%; both systems kept occupant temperature within 22–23 °C. This comparison does not establish real-world comfort or energy savings.
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