An advanced airport terminal cooling load forecasting model integrating SSA and CNN-Transformer

Abstract

The cooling system in airport terminals experiences dynamic fluctuations of cooling load, influenced by factors like outdoor environmental climate and indoor passenger flow. Therefore, establishing a precise and reliable cooling load forecast is crucial for the energy-efficient operation of the cooling system. This paper aims to develop a model for forecasting cooling loads in airport terminals, providing accurate guidelines for energy-saving and optimal operations. Firstly, Singular Spectrum Analysis is applied for data reconstruction. Secondly, a one-dimensional model with a Convolutional Neural Network Algorithm is integrated, replacing the conventional sine and cosine positional encoding. Finally, recognizing the traditional Transformer's limited responsiveness to local features, this study adopts a causal Convolutional approach as an alternative to dot product computation, followed by empirical validation using air conditioning load data from airport terminals. The experimental results demonstrate mean absolute percentage error of 2.16%, 6.39% and 10.71% for single-step, two-step and three-step predictions, respectively, confirming the reliability of the model. It demonstrates superior accuracy compared to various models and provides crucial insights for predicting cooling loads in large public structures with similar data complexity. This model can serve as a solied foundation for guiding the cooling system of airport terminals toward the objectives of energy conservation and emissions reduction.