Since the thermal environment of large space buildings such as stadiums can vary depending on the location of the stands, it is important to divide them into different zones and evaluate their thermal environment separately. The thermal environment can be evaluated using physical values measured with the sensors, but the occupant density of the stadium stands is high, which limits the locations available to install the sensors. As a method to resolve the limitations of installing the sensors, we propose a method to predict the thermal environment of each zone in a large space. We set six key thermal factors affecting the thermal environment in a large space to be predicted factors (indoor air temperature, mean radiant temperature, and clothing) and the fixed factors (air velocity, metabolic rate, and relative humidity). Using artificial neural network (ANN) models and the outdoor air temperature and the surface temperature of the interior walls around the stands as input data, we developed a method to predict the three thermal factors. Learning and verification datasets were established using STAR CCM+ (2016.10, Siemens PLM software, Plano, TX, USA). An analysis of each model’s prediction results showed that the prediction accuracy increased with the number of learning data points. The thermal environment evaluation process developed in this study can be used to control heating, ventilation, and air conditioning (HVAC) facilities in each zone in a large space building with sufficient learning by ANN models at the building testing or the evaluation stage.
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