## 5 Discussion  
The indoor temperature and relative humidity are keys to the healthy growth of solar greenhouse crops. In order to implement precise control and make irrigation decisions in advance, it is necessary to construct models to predict the indoor environmental parameters and ET\({}_{0}\)[1; 46]. Accurate prediction of indoor and outdoor environmental parameter and ET\({}_{0}\) of solar greenhouses requires complete indoor and outdoor environmental parameters of the greenhouse.  
Although many kinds of models have been developed for indoor environmental parameters of greenhouses, has all have their own drawbacks. Previously, [PERSON] et al. [12] utilized artificial neural networks for the prediction of hourly mean values of ambient temperature. [PERSON] et al. [17] compared mathematical models with artificial neural network and select the best prediction models. [PERSON] et al. [20] presented a novel temperature prediction model based on a least squares support vector machine model with parameters optimized by the improved particle swarm optimization. [PERSON] et al. [18] proposed a model to predict the temperature and humidity of a greenhouse based on improved LM-RBF. [PERSON] et al. [47] proposed the model of greenhouse temperature and parameter states. All these models mainly consider the environmental parameters inside the greenhouse and the change trend of time series. [PERSON] et al. [8] proposed three deep learning models to predicting environmental parameters change. Although these models considering the external and indoor environmental parameters of the greenhouse, the prediction time steps are only from 5 to 30 min. Most of the indoor environmental parameters (air temperature and air humidity) of solar greenhouses in developing countries can be monitored completely and accurately [13; 48]. Agricultural activities, especially crop production in greenhouse, are sensitive to the prediction time steps of the prediction model. Although in general, the shorter the prediction time, the higher the accuracy of the deep model, in the actual greenhouse crop irrigation decision and management process, the prediction time steps from 5 to 30 min are actually relatively short.  
Figure 22: The RMSE and MAE of predictive daily ET\({}_{0}\) and actual ET\({}_{0}\) for Greenhouse B under four forecast time points.  
The outdoor environmental parameters monitored by small weather stations, due to the high cost of outdoor environmental parameter acquisition equipment and the small monitoring coverage, the environmental parameters prediction models using outdoor environmental parameters and indoor environmental parameters for solar greenhouses in China are still not entirely reliable, and a universal model has not yet been reported. In this study, three deep learning time series models were built, and the hyperparameters of these models were designed using the orthogonal design. In order to verify the precision of three models under long time steps, indoor and external environmental parameters from the nearest weather station were predicted using three models in 16 datasets, the optimized model was used to predict environmental parameters under four forecast time points, 3 h, 6 h, 12 h and 24 h, respectively. The results show that the DNNR model shows the best performance for the prediction of short-term greenhouse environmental parameters. The optimized models can help users realize prediction of water consumption without the recent environmental parameters of the solar greenhouse, which can reduce dependence of prediction of greenhouse crop water consumption on environmental detection sensors.