## 4 Results and Discussion
### Comparison of the Prediction Performance for Three Models  
In order to verify the applicability of three models, each of nine DNNR models, nine LSTM models, and 18 CNN-LSTM models were trained and tested using 16 training sets and corresponding test sets. For three models, the best performing models of each training set and the corresponding test set are shown in Table 7. As can be seen from the table, the best performing DNNR model for different training and test sets is also different, and it is difficult to find the distribution rule of the best model for 16 datasets. The same conclusion applies to the LSTM and CNN-LSTM models. For humidity and temperature in two solar greenhouses, the best performing DNNR model for same training and test set is also different, the same conclusion applies to two greenhouses and the LSTM, CNN-LSTM models. For three type prediction models of Greenhouse A humidity, the RMSE and MAE of the best experimental model in 16 datasets were shown in Figure 9, it can be seen from the results, for the test set of T+3h, T+12h and T+24h, the RMSE and MAE of the best LSTM and CNN-LSTM models increase with the training set time horizon, conversely, the RMSE and MAE of the best DNNR models increases with the training set time horizon. In general, the MAE and RMSE of the best three models increase with the extension of forecast time, and the performance of DNNR was best among the three models. For three type prediction models of Greenhouse A temperature, the RMSE and MAE of the best experimental model under 16 datasets were shown in Figure 10, it can be seen from the results, for the test set of T+3h, T+6h and T+24h, the RMSE and MAE of the best LSTM and CNN-LSTM models increase with the training set time horizon. For the test set T+12h, the RMSE and MAE of the best LSTM and CNN-LSTM models decrease with the training set time horizon. Conversely, for the test set of T+3h, T+6h, T+12h and T+24h, the RMSE and MAE of the best DNNR models decrease with the training set time horizon. In general, the MAE and RMSE of the best DNNR models decrease with the training set time horizon. In general, the MAE and RMSE of the best three models also increases with the extension of forecast time, the performance of DNNR was also best among the three models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Training Set** & **Test Set** & **Number** & **Training Set** & **Test Set** \\ \hline No. 1 & T-3h\(\sim\)T-1h & No. 9 & T-3h\(\sim\)T-1h & \\ No. 2 & T-6h\(\sim\)T-1h & No. 10 & T-6h\(\sim\)T-1h & \\ No. 3 & T-12h\(\sim\)T-1h & No. 11 & T-12h\(\sim\)T-1h & T+12h \\ No. 4 & T-24h\(\sim\)T-1h & No. 12 & T-24h\(\sim\)T-1h & \\ \hline No. 5 & T-3h\(\sim\)T-1h & No. 13 & T-3h\(\sim\)T-1h & \\ No. 6 & T-6h\(\sim\)T-1h & No. 14 & T-6h\(\sim\)T-1h & \\ No. 7 & T-12h\(\sim\)T-1h & No. 15 & T-12h\(\sim\)T-1h & T+24h \\ No. 8 & T-24h\(\sim\)T-1h & No. 16 & T-24h\(\sim\)T-1h & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The sequence number and division methods of each combination of training set and corresponding test set. For three type prediction models of Greenhouse B humidity, the RMSE and MAE of the best experimental model in 16 datasets are shown in Figure 11, it can be seen from the results, for the test set of T+3h, T+6h and T+24h, the RMSE and MAE of the best LSTM and CNN-LSTM models increases with the training set time horizon. For test set T+12h, the RMSE and MAE of the best LSTM and CNN-LSTM models decrease with the training set time horizon. On the contrary, for the test set of T+3h, T+6h, T+12h and T+24h, the RMSE and MAE of the best DNNR models decrease with the training set time horizon. In general, the MAE and RMSE of the best three models also increases with the extension of the forecast time, and the performance of DNNR was also best among the three models.