Underground shopping malls, train stations, airports, etc., which have open areas to the outside, have a heavier heating and cooling load than ordinary buildings.Moreover, due to the complicated behavioral characteristics of humans, efficient heating and cooling and ventilation methods have not been established.However, due to the progress of IoT technology in recent years, the possibility of reducing CO2 emissions by new air conditioning control based on detailed data that could not be collected so far has been found.

　Kobe University, NSRI, and the Institute for Innovative Systems will jointly develop next-generation air-conditioning control technology that predicts human behavior and controls air flow, aiming for energy savings and CO50 reduction of approximately 2%.Specifically, while controlling the inflow of air at the entrance and exit according to the season and time zone, the environmental state of the underground town (human behavior and characteristics, thermal environment data, etc.) is grasped and predicted using the human flow / air flow sensor, and the prediction is performed. We will work on technological development to minimize heating and cooling consumption and reduce power consumption by smart control of air conditioning based on the results.

　Unlike conventional uniform air conditioning to a space, this technology calculates the minimum amount of heat and ventilation required for each location from the prediction of where people are, and uses airflow to reuse slightly comfortable air in the vicinity. It will be a state-of-the-art technology that adds a fan effect and performs air conditioning and ventilation with the minimum required energy.In addition, in this project, real-time data collection, analysis, and control methods using AI (artificial intelligence) will be applied to people flow prediction, airflow prediction, air conditioning, and ventilation control, and their effectiveness will be verified.