Study on choosing mobile sensor location to improve the prediction accuracy of indoor temperature distribution

Abstract

Nonuniform indoor temperature distributions have been widely introduced due to the increasing concerns brought about by the trade-off between building energy consumption and thermal comfort. Related to this, obtaining real-time indoor temperature field is crucial for control and regulation. Previously, a rapid temperature prediction algorithm based on the contribution ratio of indoor climate (CRI) and sensor information was proposed. Compared with fixed sensors, mobile sensors have better moving flexibilities and higher prediction accuracies. Given that considerable data can be obtained from one sensor, the chosen mobile sensor location has a strong correlation with the prediction accuracy. However, only a few studies have investigated the impact of the sensor location. Thus, this study aimed to determine the optimal mobile sensor location for improving prediction accuracy using the proposed algorithm. Two differently scaled rooms were numerically built, and 1.2 m high points were set as the selected locations. After experimental validation of the simulation results, three factors (moving path, supply/return air, and air velocity) were investigated to determine their effects on prediction accuracy. The results showed that the air velocity was the only significantly impactful factor. Furthermore, two principles of choosing location were proposed: (1) when the percentage of dominant velocity is high, all points should have one identical dominant direction, and (2) when the percentage of dominant velocity is low, points should have a similar actual direction. These principles were verified in both models with a prediction accuracy greater than 85%.