## 4 Conclusions  
To address the limitations of existing models in effectively capturing both inter-variable dependencies and temporal features of influencing factors in cooling load forecasting, which often result in insufficient prediction accuracy, this study proposes a hybrid forecasting approach based on iTransformer-BiLSTM. First, key indoor and outdoor influencing factors are identified through an in-depth analysis, and the iTransformer module is employed to extract features from the selected multivariate time-series data. By treating the entire  
Figure 16: SHAP Beeswarm Plot of Input Features.  
Figure 17: Feature Ranking by Mean Absolute SHAP Values.  
time series as tokens and leveraging attention mechanisms, the model captures complex nonlinear relationships among variables. Subsequently, the extracted feature representations are fed into the BiLSTM module, which utilizes bidirectional long short-term memory mechanisms to model the bidirectional dynamic characteristics of the time series, thereby generating more precise load predictions. Experimental results demonstrate that, in the task of cooling load forecasting for industrial air conditioning systems, the proposed iTransformer-BiLSTM model outperforms both single and hybrid models in key evaluation metrics, including RMSE, MAE, and SMAPE, showcasing superior predictive performance and generalization capability. Meanwhile, sensitivity analysis of the input variables further validated the model's capability to effectively identify the contribution relationships of key features in a multivariate environment, thereby enhancing the interpretability of the prediction results.  
Nevertheless, the model's actual performance is to some extent dependent on the relatively comprehensive sensor deployment and high data acquisition density currently available in the factory. The abundance of indoor and outdoor environmental data facilitates the model's ability to fully learn the patterns of load variations. Therefore, under conditions of insufficient data coverage, sparse sensor layouts, or improperly positioned sensors, the prediction accuracy and stability may be adversely affected.  
Future research could further incorporate real-time operational data to continuously optimize model parameters and introduce thermal analysis methods to systematically evaluate and optimize sensor placement and the types of monitoring data, thereby enhancing the scientific rigor of the data acquisition system and improving the quality of input features. On this basis, it is also necessary to further validate the scalability and adaptability of the model across industrial and commercial building environments of different scales and types, such as pharmaceutical workshops, shopping malls, and data centers, to enhance its engineering applicability across scenarios with varying cooling load characteristics.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON]; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] and [PERSON]. ([PERSON]); investigation, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); resources, [PERSON]; data curation, [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]); writing--original draft preparation, [PERSON], [PERSON] and [PERSON]. ([PERSON]).; writing--review and editing, [PERSON] ([PERSON]) and [PERSON]; visualization, [PERSON] and [PERSON]. ([PERSON]); supervision, [PERSON] and [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Guangdong Province, grant number 2022A1515011128.  
Not applicable.  
Due to the confidentiality requirements of the project, the research data cannot be disclosed.  
The authors declare no conflicts of interest.