Study on predicting the radiant heat flow rate of floor surface of radiant floor heating

Abstract

Radiant heat flow rate of radiant surface is crucial for radiant floor heating design and terminal selection. We found that the present empirical formula to predict radiant heat flow rate of radiant surface has limits under varied room size and surface emissivity circumstances. Wall insulation conditions also have substantial influences on the operating efficiency of floor radiant heating. Adopting machine learning algorithms and backward selection method, a two-layer Neural Network model was demonstrated to have good accuracy and requisite relevant features for new empirical formula was identified. The new formula containing the information of room depth, weighted radiant surface area, insulation conditions, indoor air temperature and radiant surface temperature as independent variables exhibits great accuracy with R-squared of 0.97 and RMSE of 2.74. The substitution of AUST with indoor air temperature can increase the prediction accuracy. Analysis reveals structural pattern and indicates interaction between wall insulation and non-radiative surface with low emissivity. We propose the use of non-radiant surfaces with low emissivity as a passive energy-saving technique for radiant floor heating. And the updated empirical formula can increase its application scenarios, and aid promote application of FRH and new passive technique.