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.
Highlights
As people spend about 90% of their time indoors [1], the indoor thermal environment becomes very important to their daily lives
Indoor temperature distribution is affected by various heat sources, which are dynamically changing with time, difficult to obtain in practice
In the study of Sasamoto et al [35], the fixed sensors were installed near each heat source, and the prediction accuracy was acceptable
Summary
As people spend about 90% of their time indoors [1], the indoor thermal environment becomes very important to their daily lives. To reduce the energy consumption of buildings while ensuring thermal comfort, creating non-uniform indoor thermal environments has been considered. In this process, the buildings’ ventilation mode gradually changes from traditional mixed ventilation (MV) to demand-oriented ventilation, such as displacement ventilation (DV) [5,6] and stratum ventilation (SV) [7,8]. Indoor temperature distribution is affected by various heat sources, which are dynamically changing with time, difficult to obtain in practice. This means that accurate boundary conditions could not be identified in advance to support CFD simulation. Considering the above, it is difficult to achieve real-time control of indoor thermal environments by this method
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