Parallel Control of Greenhouse Climate With a Transferable Prediction Model

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
$R^{2}$
, respectively, which shows the feasibility of the transferable prediction model.