## 4 Results and Discussion
### Comparison of the Prediction Performance for Three Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Greenhouse A**} & \multicolumn{2}{c}{**Greenhouse B**} \\ \cline{3-6}  & & **Humidity** & **Humidity** & **Temperature** & **Humidity** \\ \hline \multirow{8}{*}{DNNR} & No. 1 & DNNR 8 & DNNR 9 & DNNR 5 & DNNR 5 \\  & No. 2 & DNNR 5 & DNNR 4 & DNNR 5 & DNNR 2 \\  & No. 3 & DNNR 9 & DNNR 9 & DNNR 6 & DNNR 6 \\  & No. 4 & DNNR 4 & DNNR 9 & DNNR 3 & DNNR 2 \\  & No. 5 & DNNR 1 & DNNR 4 & DNNR 4 & DNNR 4 \\  & No. 6 & DNNR 6 & DNNR 9 & DNNR 6 & DNNR 8 \\  & No. 7 & DNNR 9 & DNNR 9 & DNNR 4 & DNNR 3 \\  & No. 8 & DNNR 9 & DNNR 2 & DNNR 2 & DNNR 4 \\  & No. 9 & DNNR 9 & DNNR 4 & DNNR 6 & DNNR 5 \\  & No. 10 & DNNR 8 & DNNR 2 & DNNR 6 & DNNR 9 \\  & No. 11 & DNNR 8 & DNNR 6 & DNNR 5 & DNNR 8 \\  & No. 12 & DNNR 8 & DNNR 2 & DNNR 9 & DNNR 6 \\  & No. 13 & DNNR 8 & DNNR 3 & DNNR 3 & DNNR 8 \\  & No. 14 & DNNR 4 & DNNR 2 & DNNR 6 & DNNR 8 \\  & No. 15 & DNNR 6 & DNNR 9 & DNNR 8 & DNNR 8 \\  & No. 16 & DNNR 5 & DNNR 9 & DNNR 5 & DNNR 8 \\ \hline \multirow{8}{*}{LSTM} & No. 1 & LSTM 7 & LSTM 8 & LSTM 7 & LSTM 2 \\  & No. 2 & LSTM 8 & LSTM 2 & LSTM 7 & LSTM 4 \\  & No. 3 & LSTM 9 & LSTM 1 & LSTM 3 & LSTM 4 \\  & No. 4 & LSTM 1 & LSTM 8 & LSTM 8 & LSTM 8 \\  & No. 5 & LSTM 8 & LSTM 8 & LSTM 7 & LSTM 3 \\  & No. 6 & LSTM 3 & LSTM 4 & LSTM 9 & LSTM 9 \\  & No. 7 & LSTM 7 & LSTM 2 & LSTM 4 & LSTM 6 \\  & No. 8 & LSTM 8 & LSTM 4 & LSTM 2 & LSTM 4 \\   LSTM & No. 9 & LSTM 4 & LSTM 3 & LSTM 4 & LSTM 3 \\  & No. 10 & LSTM 8 & LSTM 5 & LSTM 4 & LSTM 3 \\  & No. 11 & LSTM 9 & LSTM 8 & LSTM 7 & LSTM 3 \\  & No. 12 & LSTM 8 & LSTM 8 & LSTM 1 & LSTM 1 \\  & No. 13 & LSTM 4 & LSTM 4 & LSTM 2 & LSTM 6 \\  & No. 14 & LSTM 1 & LSTM 1 & LSTM 6 & LSTM 8 \\  & No. 15 & LSTM 5 & LSTM 5 & LSTM 7 & LSTM 1 \\  & No. 16 & LSTM 1 & LSTM 1 & LSTM 4 & LSTM 8 \\ \hline \multirow{8}{*}{CNN-LSTM} & No. 1 & CNN-LSTM 1 & CNN-LSTM 16 & CNN-LSTM 4 & CNN-LSTM 1 \\  & No. 2 & CNN-LSTM 15 & CNN-LSTM 18 & CNN-LSTM 15 & CNN-LSTM 12 \\  & No. 3 & CNN-LSTM 12 & CNN-LSTM 1 & CNN-LSTM 12 & CNN-LSTM 12 \\  & No. 4 & CNN-LSTM 2 & CNN-LSTM 3 & CNN-LSTM 8 & CNN-LSTM 9 \\  & No. 5 & CNN-LSTM 16 & CNN-LSTM 11 & CNN-LSTM 1 & CNN-LSTM 1 \\  & No. 6 & CNN-LSTM 11 & CNN-LSTM 14 & CNN-LSTM 2 & CNN-LSTM 14 \\  & No. 7 & CNN-LSTM 14 & CNN-LSTM 12 & CNN-LSTM 3 & CNN-LSTM 18 \\  & No. 8 & CNN-LSTM 13 & CNN-LSTM 18 & CNN-LSTM 8 & CNN-LSTM 14 \\  & No. 9 & CNN-LSTM 11 & CNN-LSTM 1 & CNN-LSTM 11 & CNN-LSTM 7 \\  & No. 10 & CNN-LSTM 11 & CNN-LSTM 15 & CNN-LSTM 6 & CNN-LSTM 1 \\  & No. 11 & CNN-LSTM 17 & CNN-LSTM 2 & CNN-LSTM 3 & CNN-LSTM 1 \\  & No. 12 & CNN-LSTM 5 & CNN-LSTM17 & CNN-LSTM 10 & CNN-LSTM 17 \\  & No. 13 & CNN-LSTM 14 & CNN-LSTM 4 & CNN-LSTM 16 & CNN-LSTM 1 \\  & No. 14 & CNN-LSTM 13 & CNN-LSTM 18 & CNN-LSTM 11 & CNN-LSTM 8 \\  & No. 15 & CNN-LSTM 13 & CNN-LSTM 6 & CNN-LSTM 12 & CNN-LSTM 10 \\  & No. 16 & CNN-LSTM 6 & CNN-LSTM 9 & CNN-LSTM 7 & CNN-LSTM 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The best performing models of each training set and corresponding test set. 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 DNNNR models decrease with the training set time horizon. For the of T+12h test set, the RMSE and MAE of the best DNNNR models increase with the training set time horizon. In general, the MAE and RMSE of the best three models are also increased with the extension of forecast time, the performance of DNNNR was also the best among the three models. Figure 11: The RMSE and MAE of the best experimental model for Greenhouse B humidity under 16 datasets. Figure 12: The RMSE and MAE of the best experimental model for Greenhouse B temperature under 16 datasets.