## 4 Results and Discussion
### Optimal Forecast Results at Four Forecast Time Points
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Greenhouse**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Models Type**} & \multicolumn{5}{c}{**Forecast Time Points**} \\ \cline{3-6}  & & & **T+3h** & **T+6h** & **T+12h** & **T+24h** \\ \hline \multirow{4}{*}{Greenhouse A} & \multirow{2}{*}{Humidity} & DNNR & No. 4 & No. 8 & No. 12 & No. 16 \\  & & LSTM & No. 3 & No. 7 & No. 11 & No. 13 \\  & & CNN-LSTM & No. 2 & No. 8 & No. 10 & No. 13 \\ \cline{2-6}  & \multirow{2}{*}{Temperature} & DNNR & No. 2 & No. 8 & No. 11 & No. 16 \\  & & LSTM & No. 2 & No. 8 & No. 11 & No. 13 \\  & & CNN-LSTM & No. 2 & No. 5 & No. 11 & No. 13 \\ \hline \multirow{4}{*}{Greenhouse B} & \multirow{2}{*}{Humidity} & DNNR & No. 4 & No. 8 & No. 12 & No. 16 \\  & & LSTM & No. 1 & No. 6 & No. 10 & No. 13 \\  & & CNN-LSTM & No. 2 & No. 7 & No. 12 & No. 14 \\ \cline{2-6}  & \multirow{2}{*}{Temperature} & DNNR & No. 3 & No. 7 & No. 11 & No. 16 \\  & & LSTM & No. 1 & No. 5 & No. 11 & No. 16 \\ \cline{1-1}  & & CNN-LSTM & No. 4 & No. 5 & No. 11 & No. 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The best performing models at each forecast time point. The ST of the errors of the three models under different weather conditions are shown in Figure 16, for three models, the ST of the DNNNR model is also 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 biggest at T+12 time point under different weather conditions, which means that the DNNNR was the best predictive model among the three models, and the DNNNR model was not suitable for parameters prediction 12 h ahead. For Greenhouse B humidity, 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 17. From the figure, the errors of the predictions of the DNNNR model at four forecast time points are within \([-11,6]\), \([-13,10]\), \([-19,23]\) and \([-25,27]\) %, respectively. The errors of the LSTM model predictions at four forecast time points are within \([-29,17]\), \([-21,9]\), \([-51,23]\) and \([-37,41]\) %, respectively. The errors of CNN-LSTM model predictions at four forecast time points are within \([-28,21]\), \([-26,9]\), \([-44,24]\) and \([-37,39]\) %, 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 DNNNR models, for the forecast time of T+3, T+6 and T+24, the errors of DNNNR model are smaller than the LSTM and CNN-LSTM models. The errors of the three models errors under different weather conditions are shown in Figure 18, for three models, the ST of DNNNR model is 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 biggest at T+12 time point under different weather conditions, which means that the DNNNR is the best predictive model among the three models, and the DNNNR model is not suitable for prediction of parameters 12 h ahead. Figure 13: Statistics of the error distribution between the predicted value of the best model and the actual value for the humidity of Greenhouse A.  
Figure 14: Cont. Figure 14: The ST of the three model errors for Greenhouse A humidity under different weather conditions; (**a**) 3 h advance, (**b**) 6 h advance, (**c**) 12 h advance, (**d**) 24 h advance. Figure 16: _Cont._  
Figure 15: Statistics of the error distribution between the predicted value of the best model and the actual value for the humidity of Greenhouse A.  
Figure 16: The ST of the three model errors for Greenhouse A temperature under different weather conditions; (**a**) 3 h advance, (**b**) 6 h advance, (**c**) 12 h advance, (**d**) 24 h advance. Figure 17: Statistics of the error distribution between the predicted value of the best model and the actual value for Greenhouse B humidity.