Study on statistical prediction and design method for indoor thermal environment

Abstract

The indoor thermal environment is affected by many heat factors such as occupants' activities and outdoor climate changes. Because each of them varies asynchronously, the heat factors' variation shows a nonsingle degree of freedom overall. Moreover, the heat factors' impact on space is not uniform. Therefore, there always exist spatial distributions and temporal variations. In practice, the number of degrees of freedom of an air-conditioning system is usually limited and less than that of the object zone. The control performance of an air-conditioning system designed under a perfect mixing method cannot be satisfactory because the spatial distribution is neglected. For energy conservation and thermal comfort improvement, it is necessary to propose an air-conditioning system design method which can positively use the spatial distribution and temporal variation of the indoor thermal environment. In this study, methods for indoor temperature fluctuation analysis and optimization of air-conditioner sensor location are proposed. To represent the temperature field structure, an index called CRI is integrated. With this method, the spatial distribution and temporal variation can be calculated immediately and analyzed statistically; furthermore, the indoor temperature and outdoor climate's correlation can be concisely visualized. A case study is conducted to verify the validity of this method.