Indoor condensation prediction based on a surface temperature estimation. (25th June 2019) - Hwang, Kwang‐il; Jeong, Young‐Sik; Han, Jeakyung
Summary: Since indoor condensation occurs for a variety of complex reasons, it is difficult to find a fundamental solution to prevent it. Indoor condensation, which is caused by environmental changes (an increase in internal humidity or a low ambient temperature), is difficult to prevent in an occupied residential structure based on the design of the structure. In this paper, we propose a new model for predicting indoor dew condensation that occurs in a residential environment with IoT technology. First, a basic dataset in the condensation environment is collected through a test bed, and a surface temperature estimation method that uses the machine learning model used to evaluate the dataset. In addition to the surface temperature estimation technique, which achieves a low RMSE of 0.97 in the field test, an associated condensation time prediction algorithm is proposed. The proposed method is a new method for determining the intersection point between two temperature changes based on the real‐time rate of change of the surface temperature and the dew point temperature. The high condensation prediction accuracy of the proposed method is experimentally demonstrated. Abstract : The proposed method is a new method to find the intersection point between two temperature changes considering the real‐time rate of change of surface temperature and dew point temperature.