Heat transfer prediction for radiant floor heating/cooling systems using artificial neural network (ANN)

Radiant floor cooling and heating systems (RHC) are gaining popularity as compared with conventional space conditioning systems. An understanding of the heat transfer capacity of the radiant system is desirable to design a space conditioning system using RHC technology. In the present work, a simplified heat flux model for RHC is developed for both cooling and heating modes of operation. The Artificial Neural Network (ANN) technique is used for the development of the simplified model. Experimental data from literature covering a wide operating range of the RHC is considered for model development and validation. Operating parameters such as mass flow rate (m(f)), heat resistance (R-s), mean temperature of water flowing through the pipe (T-m), and operative temperature (T-op) are considered independent variables influencing the heat flux (q(t)). The neural network consists of four input layers, one output layer, and one hidden layer with a feed-forward-back-propagation algorithm. A study on the selection of the optimum number of neurons in the range of 1-9 for the hidden layer is also performed. On the basis of the performance parameters, namely, average-absolute-relative-deviation (AARD = 0.11283) percentage, mean-square-error (MSE = 0.00055), and the coefficient of determination (R-2 = 0.9984), a hidden layer is modeled with five neurons.