## 3 Case Analysis
### Analysis of Cooling Load Characteristics and Influencing Factors  
The cooling load monitoring data for the workshop in June 2024 and the first week of June are shown in Figures 8 and 9. The data indicate that the cooling load exhibits multi-peak characteristics and periodic fluctuations, with strong nonlinearity and instability. As observed in Figure 8, the cooling load is higher during daytime working hours when workers are densely engaged in operations. During lunch and dinner breaks, the cooling load demand decreases temporarily as workers leave the workshop for meals, followed by a subsequent increase. After dinner, the cooling load demand drops to approximately 50% of the daytime peak due to a reduction in the number of workers, decreased production activities, and lower nighttime temperatures. These fluctuations are driven by multiple coupled factors:  
Impact of human behavior: Workers' schedules directly influence the intensity of air conditioning usage; Equipment operation and production tasks: The intermittent operation of production equipment, lighting, and other internal heat sources exacerbates sudden load fluctuations; Disturbance from outdoor meteorological parameters: Variations in outdoor temperature and humidity interact with the heat transfer processes of the workshop's envelope, causing disturbances to the cooling load. These factors collectively result in strong nonlinearity and complex temporal characteristics of the cooling load, making it difficult for traditional prediction methods to model the multivariable coupling relationships, leading to significant accuracy degradation in scenarios with sudden short-term fluctuations (such as during meal breaks) and long-period variations (such as night shift loads). Therefore, a nonlinear modeling approach that integrates global dependencies  
Figure 7: Distribution and Division of Temperature Points in the Factory Area. and local temporal patterns is required to meet the high-precision demands for optimizing workshop operations. This study categorizes the factors influencing the workshop's cooling load into three main categories: building structure, internal heat sources, and outdoor meteorological parameters, as shown in Table 1. For workshops already in operation, the impact of static parameters such as building structure, materials, and orientation on the factory's cooling load remains relatively stable, and thus can be simplified as constant values in the modeling process. Among outdoor meteorological parameters, solar radiation and wind speed exhibit high fluctuations and entail high monitoring costs. Therefore, in accordance with the following references [12; 36], this study includes only outdoor temperature and humidity as input variables. The dynamic factors inside the workshop (such as equipment operation, lighting systems, and personnel activity) are indirectly represented as follows: equipment operation status, personnel count, and activity intensity are embedded in the historical cooling load time-series data, while the average temperature points of the four zones and the supply and return air temperatures of air handling units reflect the real-time heat source distribution. Additionally, dimensionality reduction is applied to mitigate the risk of overfitting caused by high-dimensional inputs. A Pearson correlation analysis is conducted among these terminal parameters, outdoor meteorological parameters, date attributes, and cooling load data, as shown in Table 2.  
Figure 8: Changes in the Cooling Load of the Factory Workshop in June. Figure 9: Changes in the Cooling Load of the Factory Workshop within a Certain Week. As shown in Table 2, date attributes (such as the day of the week) have a weak impact on the cooling load and are therefore not considered as input features. Among the outdoor meteorological parameters of the factory workshop, outdoor temperature and humidity exhibit a high correlation with the cooling load, making them suitable as model input features.