Parallel Control of Greenhouse Climate With a Transferable Prediction Model
Xiaoxuan Zhao, Yingqi Han, Udom Lewlomphaisarl, Haoyu Wang, Jing Hua, Xiujuan Wang, Mengzhen Kang
Abstract
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.