## 3 Results and Discussion
### Real-Time Prediction of Micro-Environment
To enhance generalizability and long-term reliability, future work will focus on incrementally incorporating additional real-world measurement data into the training process to further improve the ANN model's robustness. Additionally, the prediction time for daily data, comprising 96 data points, was less than 5 s by using a 1.6 GHz CPU with 16 GB RAM. Based on this trained LSTM network, any 24 h time-sequence macro-environmental temperature and RH data can be input to predict the corresponding micro-environmental ones inside the storage enclosure. The model evaluation above indicated the accuracy of micro-environmental prediction under the worst-case scenario without any buffering effect from paper-based collections. The following results focus on assessing the model's robustness in practical applications involving enclosures with collections inside. Figure 15 shows data comparison between onsite measurement and ANN prediction. Notably, the micro-environment in the return air zone exhibits better stability compared to that in the supply air zone. Associated absolute errors are \(\pm\)0.6 \({}^{\circ}\)C & \(\pm\)1.6 %RH for the return air zone and \(\pm\)0.9 \({}^{\circ}\)C & \(\pm\)3 %RH for the supply air zone. These prediction accuracies \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{MSE}\) & \(\mathbf{MAE}\) & \(\mathbf{StD}\) \\ \hline Training data & 0.999 & 0.035 & 0.001 & 0.023 & 0.035 \\ (temperature) & 0.984 & 0.364 & 0.132 & 0.230 & 0.364 \\ Training data (temperature) & 0.965 & 0.037 & 0.001 & 0.025 & 0.037 \\ Testing data (RH) & 0.963 & 0.468 & 0.219 & 0.313 & 0.468 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance results of the LSTM network. are smaller than the onsite monitoring accuracy of \(\pm\)1 \({}^{\circ}\)C and \(\pm\)3 %RH, which is deemed acceptable for the real-time micro-environment prediction. Figure 13: Evaluation of the optimal LSTM in the train set. (**a**) Comparison between actual values and predicted values; (**b**) errors; (**c**) error histogram. Figure 14: Evaluation of the optimal LSTM in the test set. (**a**) Comparison between actual values and predicted values; (**b**) errors; (**c**) error histogram. Figure 15: Data comparison between onsite measurement and ANN prediction. (**a**) Return air zone; (**b**) supply air zone. In addition, the temperature and humidity fluctuations in the return air zone range from 0.75 to 1 \({}^{\circ}\)C and 1 to 11 %RH, while the supply air zone has fluctuations of 2.5 to 3 \({}^{\circ}\)C and 4 to 13 %RH. Despite the larger fluctuations in the supply air zone, deviations of both zones are negative values with the critical region of approximately 0.3 \({}^{\circ}\)C and 1 %RH. It means that the measurement temperature and RH bands are encompassed within the prediction ones for any 24 h period, thus triggering no alarms. This real-time prediction provides a feasible way to monitor the micro-environment, and to map the hygothermal condition over multiple enclosures in the macro-environment, the storage room. This would easily allow us to test relaxing the macro-environmental control to reduce operational energy consumption of the precision air-conditioning system without risking damaging the collection by doing it in reality. Consequently, this ANN prediction is robust in conformity monitoring of the micro-environment and protecting the collections stored inside the enclosures. Furthermore, this prediction contributes to the implementation of relaxed control in macro-environment, thereby promoting sustainable preventive conservation practices.