Xu F., Sato Y., Sakai Y., Sabu S., Kanayama H., Satou D., Kansha Y., 2022, A Prediction Model for Temperature Variation and Distribution Using Soft Sensing Method, Chemical Engineering Transactions, 94, 811-816.
Excessively high or low local indoor temperature causes discomfort to the users and wastes energy. Thus, it is significant to use an accurate and fast soft sensor to measure and control the local indoor temperature. In this research, the temperature variation and distribution were experimentally investigated, and an inferential model was developed as a soft sensor to predict the local temperature. To simulate the indoor temperature environment under refrigeration mode of air conditioner, 10 oC cold water was continuously fed to an acrylic box filled with water at two initial temperatures of 25/35 oC. Transient temperatures of different locations were simultaneously measured and the water flow pattern was observed by the particle image velocimetry method. The correlation coefficients between transient temperatures at different locations were examined for the variable selection. A multiple regression model was developed using the least squares method and validated by the measured transient temperatures. The experimental data agreed well with the prediction model. Therefore, this method can contribute to the air conditioning system design.