Temperature Prediction of a Temperature-Controlled Container with Cold Energy Storage System Based on Long Short-Term Memory Neural Network  
[PERSON]  
1 College of Engineering, South China Agricultural University, Guangzhou 510642, China; [EMAIL_ADDRESS] (J.G. )1 College of Engineering, South China Agricultural Machinery and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China 12 Key Laboratory of Key Technology of Southern Agricultural Machinery and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, China 2  
[PERSON]  
1 College of Engineering, South China Agricultural University, Guangzhou 510642, China; [EMAIL_ADDRESS]. also help maintain quality by reducing temperature fluctuations in the storage or transport process, effectively reducing the loss of fresh products [4]. Temperature control is crucial for maintaining the quality of fruits and vegetables during storage and transportation [7]. Accurate temperature prediction can enhance the operation of intelligent temperature control systems [8], thereby improving system efficiency. Numerous studies are addressing this issue. [PERSON] et al. predicted temperature variations at different locations of a phase change refrigeration holding tank by developing a one-dimensional analytical model [9; 10; 11]. [PERSON] et al. developed a packaging structure thermal resistance calculation model for predicting refrigerant mass calculations [12; 13]. Temperature prediction in cold energy storage facilities is challenging because the thermal characteristics of the PCM are complex during the cold energy release process, which is also coupled with the ambient environment and the products [14]. On the other hand, describing the heat transfer process and making temperature predictions for a cold energy storage system through physical modeling can be difficult to achieve due to the phase transition and complex heat transfer process of PCM. An intelligent prediction method can compensate for the limitations of mathematical methods, which often struggle with parameter fitting due to the complexity of the mechanisms [15]. Moreover, using neural network models to predict the temperature of complex heat transfer systems can simplify the prediction process and obtain temperature prediction results without considering the specific heat transfer behavior of complex systems. In recent years, neural network methods have been widely used in research on temperature variation prediction. [PERSON] et al. [16] predicted the thermal efficiency of a novel straight-through evacuated tube collector using a neural network model; several artificial neural network techniques were proposed to predict the thermal performance of an all-glass, straight-through evacuated tube solar collector. Their research shows that the relative error between the radial basis function (BBF) model and the actual value is the smallest, followed by the propagation (BP) model and support vector regression (SVR) model, with R\({}^{2}\) values of 0.9658, 0.9059, and 0.8447, in that order, which also means that these models can match the experimental data well. [PERSON] et al. [17] developed an artificial neural network (ANN) model to determine the required time for increasing the current indoor temperature to the setback temperature. In addition, the effect of different parameters on the prediction performance was discussed. The optimal number of hidden layers, the optimal number of hidden neurons, the learning rate, and the moments were 1, 7, 0.6, and 0.7, respectively, and the R\({}^{2}\) of the prediction using the ANN model was more than 0.9 after parameter optimization. Long short-term memory (LSTM) [18], proposed by [PERSON] and [PERSON] in 1997, is a type of recurrent neural network (RNN) composed of memory cells and control units. It is designed to predict future data changes by extracting historical features from time-series data, making it well suited for time-series data processing. [PERSON] et al.