Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

Abstract

In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

Approach on prediction of indoor temperature distribution by combining POD, CRI and mobile sensors

“…The neutral temperature is defined as 26℃. The numerical method used in this case is the same as the previous study, and its reliability has been verified [2]. No more details here.…”

“…Under this condition, it can be considered that the heat transfer of each heat source in the space changes linearly with the heat source intensity, that is, the CRI of each heat source is a fixed value. Accordingly, a method based on CRI and finite air temperature collected by one mobile sensor to predict indoor temperature distribution has been proposed [2]. The temperature rise or drop from n T at the location j X can be expressed by Equation ( 2),…”

“…A method based on Contribution Ratio of Indoor Climate (CRI) and finite air temperature to quickly predicting the indoor temperature distribution has been proposed and improved [1][2]. However, The basic premise that CRI can be used to evaluate and predict the indoor temperature distribution is that in the steady airflow field, the CRI can be considered as a fixed value [3][4].…”