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The temperature fluctuation during the low-temperature storage process of citrus is a key factor that triggers quality and safety risks for the fruit and increases refrigeration energy consumption. Simultaneously, quality and energy consumption are crucial evaluation indicators for assessing the efficiency of citrus cold storage. Achieving dynamic predictions for both aspects can provide reliable support for scientifically anticipating and precisely optimizing citrus cold storage efficiency. In light of this, a citrus cold storage efficiency time-series prediction model was proposed based on PatchTST. Firstly, a basic PatchTST model was constructed based on the self-attention mechanism and the channel independent (CI) prediction method. Secondly, by integrating the basic PatchTST model with the covariate feature extraction module from the TiDE model, feature extraction for all sequences in the multivariate time series dataset was achieved, effectively improving the model’s prediction accuracy. Finally, quantitative analysis of the correlation between cold storage refrigeration parameters, energy consumption, and citrus temperature was conducted by using the Pearson correlation analysis method. This analysis helped determine the input parameters for the TiDE-PatchTST model. The model was then trained and tested with 5000 sets of experimental data, and its accuracy and superiority were compared and validated against other models like basic PatchTST and Informer. The results showed that the predicted cold storage energy consumption values of the TiDE-PatchTST model had average absolute errors (MAE) and root mean square errors (RMSE) of 3.645W·h and 10.421W·h, respectively. The MAE and RMSE for citrus temperature predictions were 0.034℃ and 0.042℃, respectively. Compared with Transformer model, the MAE and RMSE in energy consumption predictions were decreased by up to 41.43% and 39.27%, and in citrus temperature predictions, they were decreased by up to 46.03% and 28.81%. The research result can provide strong support for the dynamic perception and optimization control of temperature fluctuations and energy consumption during the citrus cold storage process.