The air conditioning system constitutes more than half of the total energy demand in hub airport buildings. To enhance the energy efficiency and to enable intelligent energy management, it is vital to build an accurate cooling load prediction model. However, the current models face challenges in dealing with dispersed load patterns and lack interpretability when black box approaches are adopted. To tackle these challenges, we propose a novel k-means-Temporal Fusion Transformer (TFT) based hybrid load prediction model. Specifically, the daily load patterns are grouped using an improved k-means clustering method that considers both input feature weights and dynamic time warping (DTW) distances. Additionally, the statistical features of the clustering output are inputted into the TFT. By further incorporating context information, the integration of data between different schema categories is achieved, thus reducing errors that may occur during the transition process. As a result, the prediction performance and interpretability are significantly improved. The Chongqing Jiangbei Airport T3A terminal is used as a case study, and experiments are conducted using cooling data from the No.1 energy station, as well as the airport traffic data and the meteorological station data. Results are compared with other mainstream models, confirming that the proposed day-ahead load forecasting model achieves improvements in several performance indicators, including MAE, MAPE, CV-RMSE, and R2, which are 384 kW, 3%, 5%, and 0.058 respectively.

中文翻译：

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基于变压器的蓄热枢纽机场空调系统日前冷负荷预测

空调系统占枢纽机场建筑总能源需求的一半以上。为了提高能源效率并实现智能能源管理，建立准确的冷负荷预测模型至关重要。然而，当前模型在处理分散负载模式方面面临挑战，并且在采用黑盒方法时缺乏可解释性。为了应对这些挑战，我们提出了一种新颖的基于 k-means-Temporal Fusion Transformer (TFT) 的混合负载预测模型。具体来说，使用改进的 k 均值聚类方法对每日负载模式进行分组，该方法同时考虑输入特征权重和动态时间扭曲 (DTW) 距离。此外，聚类输出的统计特征被输入到TFT中。通过进一步合并上下文信息，实现了不同模式类别之间数据的集成，从而减少了转换过程中可能出现的错误。结果，预测性能和可解释性显着提高。以重庆江北机场T3A航站楼为案例，利用1号能源站的制冷数据以及机场交通数据和气象站数据进行实验。结果与其他主流模型进行比较，证实所提出的日前负荷预测模型在MAE、MAPE、CV-RMSE和R2等多个性能指标上取得了改进，分别为384 kW、3%、5%和0.058分别。