This study developed a gradient-boosted regression tree-based artificial intelligence (AI) model--temperature reduction effect AI model (TREAM)--to determine the temperature reduction effect of fog cooling that varies with weather conditions. According to the trend of global warming, our society is suffering from serious damage from urban heat islands, especially negatively affecting human health and thermal comfort. Therefore, it is very important to develop and evaluate adaptive technology for providing pleasant thermal comfort to humans. This study select fog cooling as adaptive technology for human thermal comfort, and indoor and outdoor simulation were performed using STAR CCM+, a computational fluid dynamics(CFD) program. Moreover, transient analysis to validate the outdoor model of CFD and parametric study to identify the correlation between the environmental factors and fog cooling were performed. When initial temperature set as 45 degrees C at a relative humidity of 90% and wind speed of 1 m/s, a temperature reduction of 8.92% can be obtained. Regardless of the temperature and humidity conditions, the temperature reduction effect of fog cooling was similar to 1% if the wind speed increased above 5 m/s. This study contributed to the quantitative analysis of the temperature reduction effect according to the change of environmental factors.
Kim, J; Kang, J
AI based temperature reduction effect model of fog cooling for human thermal comfort: Climate adaptation technology
Sustainable Cities And Society