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To facilitate an increase in comfortable and healthy indoor environments, a lot of people have researched and conducted studies related to this field. However, it has proven difficult to find optimal indoor conditions because each of the elements that make up the thermal environment has different properties. Natural ventilation is an important factor in meeting and creating the thermal environment as well as improved indoor air quality. Moreover, the continuation of efforts to reduce energy consumption in buildings is also required from the design stage due to the depletion of energy resource and global warming. Therefore, this study aims to provide a material of natural ventilation for educational facilities by calculating the energy consumption of a classroom in accordance with indoor temperature distributions in summer. A target educational facility with classrooms was selected in Seoul, Republic of Korea. This study assumed that the classroom windows were fully opened and were then analyzed for indoor temperature distributions and energy consumption according to natural ventilation. In addition, the simulation was conducted based on the actual daily schedule in the school. The results were achieved by utilizing the Computer fluid dynamics simulation and Building energy simulation. This paper will be the basis of research related to occupant behavior and patterns indoors. [ABSTRACT FROM AUTHOR]

A lot of studies have been made to enhance the similarity between the target facility and building energy simulation (BES) model for an analysis of exact energy savings. Towards this end, ASHRAE guideline 14 presented the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) as criteria by which the similarity between the target facility and BES model can be measured. However, since the criteria of the CV(RSME) were met through manual and iterative performances in previous studies, this posed a disadvantage in that a lot of time and effort were consumed. In this regard, this study proposed an automatic calibration model for approaching the minimum CV(RMSE) using the BES model and optimization algorithm. The framework was conducted in five steps as follow: (i) collecting the target facility information; (ii) establishment of the BES model; (iii) calibration of the BES model in accordance with the CV(RMSE); (iv) setting the design variables and objective functions; (v) development of the automatic calibration model using optimization algorithm. As a result, the CV(RMSE) was automatically reduced from 18.10% to 12.62%. The proposed model improved calibration between actual energy data and simulation energy data while reducing time consuming. [ABSTRACT FROM AUTHOR]