## 8 Conclusions  
This study was based on the temperature monitoring data from a temperature-controlled container with a cold energy storage system. A long short-term memory network (LSTM) temperature prediction model was built and compared with the physical model. After that, the dataset for training was optimized to achieve a more accurate prediction of the temperature in the container, and the conclusions were drawn as follows. Compared with the physical model, the LSTM model predicted the trend with higher accuracy, the RMSE, MAE, and MAPE were reduced by 3.752, 2.451, and 0.248, respectively, the R\({}^{2}\) improved by 0.642, and the relative error in predicting the status of the CCB (cold chain breaking) temperature was reduced by 3.92%. The optimization results of the model dataset for the temperature prediction in the fresh-keeping environment showed that it is recommended to use the initial 20% of the historical temperature data to predict the future 80% of the temperature data, with a training elapsed time and R\({}^{2}\) of 15.5 min and 0.997, respectively. The LSTM model was able to achieve accurate predictions of the status of CCB for PCM in the cold storage plates and the fresh products with a relative error of 1.05% and 0.45%, respectively. The above shows that the temperature prediction of temperature-controlled containers with a cold storage system using an LSTM model has high accuracy. However, there are still points that need to be improved. The LSTM model is not adept at handling a long series of data; the prediction accuracy of the LSTM model is affected when the time demand for temperature prediction is long. In addition, if it is possible to select the training set separately according to the phase transition phase and the non-phase transition phase, then this will be beneficial in improving the accuracy of the temperature prediction and reduce the training time. Finally, it is worthwhile to test different neural network models for temperature prediction because more efficient neural networks perform better in applications. Conceptualization, J.G. and S.L. ; methodology, S.L. ; software, S.L. ; validation, J.L. ; formal analysis, J.G. ; investigation, S.L., J.L. and D.L. ; resources, W.Z. and J.G. ; data curation, D.L. ; writing--original draft preparation, D.L. ; writing--review and editing, J.G. ; visualization, D.L. ; supervision, J.G. ; project administration, W.Z. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangdong Province 2019 Provincial Agricultural Science and Technology Innovation and Extension Project, grant number 2023 KJ101; the Innovative Team for Common Key Technology Research and Development of Agricultural Product Freshness and Logistics, grant number 2023 KJ145; the National Science Foundation of China, grant numbers 31901736 \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**Time to Reach the**} & \multirow{2}{*}{**Relative Error in**} \\  & & & & & **Dredicted** & & **Actual** \\ \hline PCM & 1.442 & 0.975 & 0.121 & 0.450 & 52.98 & 53.54 & 1.05 \\ Fresh products & 0.091 & 0.087 & 0.010 & 0.981 & 61.52 & 61.24 & 0.45 \\ Inlet & 0.223 & 0.212 & 0.021 & 0.944 & - & - & - \\ Outlet & 0.041 & 0.029 & 0.003 & 0.999 & - & - & - \\ Surface of cold storage plates & 0.991 & 0.640 & 0.079 & 0.746 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training and prediction results. and 31971806; the Maoming Laboratory Autonomous Scientific Research Project, grant number 2021 ZZ003; the Natural Science Foundation of Guangdong Province, grant number 2020A1515010967; and topics of R&D in Key Areas of Guangzhou Municipality, grant number 2023B01J2001. Not applicable. The data presented in this study are available on request from the corresponding author.