발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.6] A Study on the Effect of the Horticultural Switchgear on the Predicted Temperature and Humidity of Machine Learning Infrastructure |
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| 논문투고자 |
Gwang-Hoon Jeong, Jeong-Hun Seo, Hyun Yoe, Meong-Hun Lee, Jang-Woo Park |
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| 논문내용 |
In this paper, based on the collection of environmental data in 1-minute increments for about 7 months from January 01, 2021 to June 27, 2021 in a facility horticultural smart farm of a farmhouse in Naju-si, Jeollanam-do, random forest algorithm was used to The correlation of temperature and humidity with the driver and environmental data characteristics of facility horticulture was obtained to find out which data characteristics have an influence on temperature and humidity, and to find the predicted value of temperature and humidity one hour later. Although the actuator and environmental data characteristics of facility horticulture affect the change in temperature and humidity, the actual and predicted values of each temperature and humidity were compared by removing variables that did not have much influence and variables that had much influence among them. As a result obtained through this, it was found that the predicted value of temperature and humidity is greatly influenced by the top_moter, that is, the skylight opener. Therefore, the predicted values of temperature and humidity one hour later vary greatly depending on whether the switch is operated or not, and it can be seen that to predict the temperature and humidity, not only the temperature and humidity of one hour ago, but also whether the switch is operated or not is required. In future research, we plan to predict more accurate temperature and humidity through many actuators and environmental data characteristics and various algorithms. |
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