In a closed space such as inside a building, various environmental factors such as population density and the degree of light penetration depending on the weather and time affect the efficiency of work and learning.Therefore, in order to optimize the learning environment, it is important to respond quickly to changes in environmental factors.

　Therefore, Seikei University and NTT Communications will conduct a demonstration experiment to derive comfortable temperature / humidity, illuminance, CO2 concentration, etc. during learning by analyzing various data in the university collected from the "EnOcean" sensor.

　In the experiment, data such as temperature and humidity, illuminance, and CO30 concentration were collected from about 2 "EnOcean" sensors installed in university lecture buildings and libraries.It is stored on NTT Communications' cloud service "Things CloudR" for IoT.Based on the accumulated data, Professor Ogawa's laboratory at Seikei University, which specializes in fluid mechanics that grasps and analyzes the flow of air, analyzes temperature, humidity, and airflow, which is a comfortable space for students and faculty members.On the other hand, NTT Communications is in charge of data analysis such as illuminance and CO2 concentration.

　After that, by combining the analyzed data and the experience evaluation obtained from the questionnaires to students and faculty members, the conditions for realizing the optimum learning environment are identified, and the "value of space", which is the most comfortable learning environment, is maximized. Plan.Through this demonstration experiment, Seikei University aims to formulate "environmental space guidelines" that set optimal values ​​for various spatial data such as temperature and humidity so that students and faculty members can lead comfortable lives at the university.