温室
控制(管理)
计算机科学
环境科学
湿度
气候变化
气候模式
人工智能
气象学
地理
生态学
园艺
生物
作者
Xiaoxuan Zhao,Yingqi Han,Udom Lewlomphaisarl,Haoyu Wang,Jing Hua,Xiujuan Wang,Mengzhen Kang
出处
期刊:IEEE journal of radio frequency identification
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:6: 857-861
被引量:6
标识
DOI:10.1109/jrfid.2022.3204363
摘要
Highly intelligent greenhouse without human intervention is the goal of autonomous greenhouse control. In this paper, a parallel control framework for greenhouse climate is proposed which aims to minimize the need for monitored data and expert knowledge. GreenLight climate model is used as a knowledge-based model that produces simulated data. LSTM with control units is pre-trained with these data. Test on necessary data size is done by transferring the model to other greenhouses. The new transferred model has a good improvement in the prediction of indoor temperature, humidity and CO2 concentration with approximate 0.05, 0.05 and 0.1 of R2, respectively, which shows the feasibility of the transferable prediction model.
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