Feng Guoce, Zhang Lei, Ai Feifan, Zhang Yirui, Hou Yupeng
School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China.
Entropy (Basel). 2022 Aug 12;24(8):1111. doi: 10.3390/e24081111.
A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed.
降低高铁能耗并提高热舒适度的一个关键因素是控制空调温度。准确预测送风温度旨在提高控制效果。现有的送风温度预测模型研究是跨学科的，涉及传热科学和计算机科学，该问题被定义为时间序列预测。然而，该模型被广泛认为是一个复杂的非线性动态模型。这使得现有的统计和深度学习方法，如自回归积分移动平均模型（ARIMA）、卷积神经网络（CNN）和长短期记忆网络（LSTM），难以充分捕捉这些变量之间的相互作用并提供准确的预测结果。最近的研究表明Transformer在提高预测能力方面具有潜力。本文针对高速列车车厢送风温度提出了一种改进的时态融合Transformer（TFT）预测模型来应对这些挑战，有两点改进：（i）基于扩张因果卷积的双卷积残差编码器结构；（ii）基于门控线性单元的时空双门控结构。此外，本研究设计了一种适用于温度预测的一般长序列时间序列预测任务的损失函数。使用中国特定地点的高铁空调运行数据集进行的实证模拟表明，使用改进的TFT模型对两个单元的温度预测分别比原模型提高了21.70%和11.73%的平均绝对百分比误差（MAPE）。此外，实验表明该模型在时间序列计算任务上有效优于七种流行方法，并分析了时间维度上预测问题的关注度。
