关键词: 地采暖地板, 封闭腔, 温度场预测, 神经网络

Abstract: [Objective] Based on the temperature data of the finite points obtained from the test, BP neural network is used to predict and analyze the temperature field in the closed cavity in the time and space dimensions, so as to provide the theoretical and data support for the thermal storage performance analysis of wood floor.[Method] Based on the equipment for testing the thermal released field by the wood floor which was developed by the author's research group, the temperature data in the closed cavity is acquired and divided into training and testing sets of neural network. In the time dimension, the coordinates and the temperature values of the first three time nodes are taken as inputs and the temperature value of the fourth time node is defined as the output. In this process, the previous 80 sets of temperature data are defined as training set while the next 28 sets of temperature data are defined as testing set. In the space dimension, temperature data of 4/5 of the total sensors are chosen as training set while the remaining 1/5 of the total sensors are defined as testing set. The BP networks of time and space are constructed based on the training set, respectively. Furthermore, the constructed models could be validated according to the testing set.[Result] In the time dimension, the computed errors and R² are as following: mean relative error(MRE)=0.269 2%,maximum relative error(MAE)=5.916 0%,mean square error(MSE)=0.422 4%,fitting degree(R²)=0.998 7. In the space dimension, MRE=0.364 2%,MAE=4.781 8%,MSE=0.521 9%,R²=0.998 5.[Conclusion] The prediction result derived by BP neural network are adequately reliable, demonstrating that this method can effectively obtain the continuous and complete temperature field inside the measurement cavity of the wood floor, thus a new theoretical support is provided for the analysis and calculation of the thermal storage performance of the wood floor.

Key words: wood flooring for ground with heating system, closed cavity, temperature field prediction, artificial neural network