A TSception–Transformer–TCN-Based Temperature-Prediction Method for Enclosed Cold-Aisle Data-Center Rooms

In enclosed cold-aisle data-center rooms, hotspot temperatures within the cold aisles are directly related to server inlet air safety and air-conditioning operation performance. Therefore, high-accuracy temperature prediction is of great significance for thermal-risk warning and operational optimization. This study focuses on an enclosed cold-aisle data-center room in a large data center located in a hot-summer and warm-winter region. The maximum temperature of one selected cold aisle is taken as the prediction target, and a hybrid prediction model integrating TSception, Transformer, and TCN is proposed to characterize multi-scale local disturbances and cross-period temporal dependencies. The model inputs and historical time window are determined through cold–hot separation analysis, cold-aisle temperature-stability comparison, lag analysis between supply air temperature and cold-aisle temperature, and correlation analysis of candidate variables. The experimental results show that the proposed model achieves an MAE of 0.285 °C, an RMSE of 0.438 °C, an NRMSE of 4.76%, and a MAPE of 1.13% in predicting the cold-aisle temperature of the AB aisle, outperforming Persistence, BP, LSTM, XGBoost, and several ablation models. Further multi-aisle experiments demonstrate that the proposed model consistently maintained low prediction errors in independent prediction tasks across different cold aisles, indicating good applicability of the model architecture for characterizing temperature time series in multiple cold aisles within the same data-center room. The proposed method can provide a reference for hotspot-risk identification in enclosed cold aisles and operational optimization of precision air-conditioning systems.