## 1 Introduction  
As the scale of the manufacturing industry expands and energy consumption demands rise, air conditioning system energy consumption accounts for 30-50% of the total energy consumption in factory buildings [1, 2], making it a significant source of carbon emissions in factories. However, traditional building automation systems rely on real-time feedback regulation and lack the foresight to predict cooling load trends. This results in delayed responses during significant fluctuations in cooling load [3], leading to energy waste, reduced operational efficiency, and difficulties in maintaining indoor thermal comfort. Accurate cooling load prediction can optimize air conditioning system parameters (e.g., chiller start/stop times, pump frequency adjustments) in advance, dynamically matching actual demand and reducing redundant cooling. Meanwhile, the multi-peak characteristic, periodic fluctuations, and timely disturbances (e.g., during meal times) of factory cooling load require the model to capture the multivariable coupling relationships and complex time-series features that affect the load. Therefore, precise cooling load prediction is a key technology for energy-saving and consumption reduction in factories. It not only reduces building energy costs and extends equipment lifespan but also improves thermal comfort in workshops, ensures production processes, and supports the manufacturing industry's transition to low-carbon and intelligent operations, making it of significant practical importance for achieving dual-carbon goals. Cooling load prediction is an important part of designing and operating building air conditioning systems. There are currently two main approaches: mechanism-based modeling and data-driven modeling [4, 5], each suitable for different application fields. Mechanism-based modeling relies on the physical characteristics of buildings and thermodynamic principles to describe the internal thermal equilibrium processes through mathematical equations. It requires detailed information such as building structure, internal heat source distribution, usage characteristics, and external environmental conditions to establish a building thermodynamic model. Tools such as EnergyPlus [6], TRNSYS [7], and DeST [8] are often used to simulate building cooling load. However, due to difficulties in obtaining building parameters, high computational complexity, and limited flexibility, mechanism-based modeling is typically used during the air conditioning design phase and is less suitable for dynamic cooling load prediction during the operational phase. Data-driven modeling uses historical data from air conditioning systems to establish load prediction models through machine learning and deep learning techniques. This approach offers advantages such as data intuitiveness, high flexibility, fast modeling, and strong learning capabilities, making it widely used in energy-efficient operation optimization of air conditioning systems in recent years. Numerous scholars both domestically and internationally have conducted extensive research on data-driven modeling for air conditioning cooling load prediction, evolving from single-method data-driven modeling to combined modeling methods. The single data-driven modeling approach has evolved from early classical statistical methods to machine learning techniques, such as Support Vector Regression [9, 10, 11, 12], and BP Neural Networks [13, 14, 15]. In recent years, deep learning methods have also become a prominent research focus in the field of air conditioning cooling load prediction. With the increasing accuracy requirements for load prediction, multivariable data fusion, hybrid model optimization, and improvements in hyperparameter optimization algorithms have become key development directions for air conditioning cooling load prediction models. [PERSON] et al.