## 4 Results and Discussion
### Optimal Forecast Results at Four Forecast Time Points  
For each forecast time points (T+3h, T+6h, T+12h and T+24h), the predictive performances of all experimental models were compared, and the best model at each forecast time point was shown in Table 8; for Greenhouse A temperature prediction, the best models for each of the three different models are similar at each forecast time point. At the same time, for other parameters in two greenhouses, the best model for each of the three different models was not exactly the same at each forecast time point. For the humidity of Greenhouse A, the statistics of the error distribution between the predicted value of the best model and the actual value at four forecast time points are shown in Figure 13. From the figure, the errors of the predictions of the DNNR model at four forecast time points are within \([-8,10]\), \([-12,13]\), \([-44,49]\) and \([-19,26]\), respectively. The errors of LSTM model predictions at four forecast time points are within \([-19,8]\), \([-22,13]\), \([-26,45]\) and \([-41,34]\), respectively. The errors of CNN-LSTM model predictions at four forecast time points are within \([-21,16]\), \([-25,18]\), \([-28,41]\) and \([-31,34]\), respectively. The errors of the three models increase with the prediction time, specifically, for the forecast time of T+12, the errors of the LSTM and CNN-LSTM models are smaller than the DNNR models, for the forecast time of T+3, T+6 and T+24, the errors of the DNNR model are smaller than the LSTM and CNN-LSTM models. In order to analyze the correlation between the errors of the three forecasting models and the weather conditions, for the humidity of Greenhouse A, the ST of the three model errors under different weather conditions are shown in Figure 14; for three models, the ST of the DNNR model was the smallest under different weather conditions, and the ST of three models increase with the training set time horizon, the ST of three models are the largest at T+12 time point under different weather conditions, which means that the DNNR was the best predictive model among the three models, and the DNNR model was not suitable for parameter prediction 12 h ahead. For the temperature of Greenhouse A, the statistics of the error distribution between the predicted value of the best model and the actual value at four forecast time points are shown in Figure 15. From the figure, the errors of the predictions of the DNNR model at four forecast time points are within \([-5,8]\), \([-6,6]\), \([-8,12]\) and \([-7,7]\)\({}^{\circ}\)C, respectively. The errors of LSTM model predictions at four forecast time points are within \([-6,9]\), \([-6,10]\), \([-18,17]\) and \([-13,11]\)\({}^{\circ}\)C, respectively. The errors in the predictions of the CNN-LSTM model at four forecast time points are within \([-4,7]\), \([-3,9]\), \([-14,12]\) and \([-15,8]\)\({}^{\circ}\)C, respectively. The errors of the three models increase with the prediction time, specifically, for the forecast time of T+12, the errors of the LSTM and CNN-LSTM models are also smaller than the DNNR models. For the forecast time of T+3, T+6 and T+24, the errors of the DNNR model are smaller than the LSTM and CNN-LSTM models.