## 1 Introduction
[16] proposed an LBF cooling load combined prediction model based on LSTM, BP, and particle filters, which integrates the advantages of multiple algorithms and achieves superior prediction accuracy and robustness in air conditioning load forecasting applications. [PERSON] et al. [17] proposed a hybrid sequence learning prediction model that combines CNN and GRU. By utilizing CNN to extract key features from the data and inputting them into a multi-layer GRU for sequence learning, this method outperforms the single GRU model in terms of accuracy and generalization ability. References [18, 19, 20] also employ CNN to extract features from the input data, reducing input dimensions while improving prediction accuracy. Although CNN can effectively extract local features, its ability to capture global information is limited [21], which may affect prediction accuracy in nonlinear and dynamic load forecasting tasks. Moreover, traditional sequence models (such as LSTM and GRU) can alleviate the vanishing gradient problem [22], but still face issues such as high computational cost, limited capacity to capture long-range dependencies, insufficient global feature modeling, and poor scalability when handling long sequences. The Transformer model has demonstrated significant advantages in global information modeling and long-range dependency capture due to its powerful self-attention mechanism [23]. However, it still has limitations in local feature extraction and model stability. Traditional Transformer prediction models typically integrate the time-step variable features (Tokens) in a time series into a single high-dimensional vector (Temporal Token), encode the time-step features through a feed-forward network (FFN), and capture the relationships between time steps using attention mechanisms [24]. However, this method has significant flaws: variables with different physical meanings are forced into the same Token, which can introduce noise due to distribution differences or time lags, making it difficult to accurately represent the interactions between variables. Moreover, processing Tokens in time-step order leads to high computational complexity and inefficiency [25], and the limited local receptive field prevents the model from effectively capturing long-sequence dependencies. In recent years, although some studies have combined Transformer and RNN models or applied them individually in fields such as water level prediction [26; 27; 28], power output forecasting [29; 30], and trajectory prediction [31; 32], their applications in HVAC systems, particularly in cooling load prediction tasks for manufacturing factories, remain limited. Compared to the datasets in the aforementioned fields, cooling load data from factory workshops exhibit pronounced nonlinearity, multi-peak periodic fluctuations, and strong multivariate coupling characteristics. These include the intertwined effects of outdoor meteorological conditions, indoor environmental parameters, and variations in production processes, which collectively lead to highly complex and challenging time-series modeling. Existing models based solely on Transformer or RNN architectures face limitations in this context, often demonstrating insufficient local feature extraction or inadequate global dependency capture when modeling such multi-source heterogeneous time-series data, thereby restricting further improvements in prediction accuracy. Therefore, this paper proposes an iTransformer-BiLSTM hybrid prediction method for cooling load, using iTransformer to capture the global nonlinear relationships between multiple variables and BiLSTM to extract the time-series local features of the variables. This method achieves global-local feature collaborative modeling, significantly improving cooling load prediction accuracy under complex conditions and providing effective technical support for air conditioning energy optimization in manufacturing factories.