## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Model for Prediction
#### 4.4.5 Transformer  
The cornerstone of the Transformer model lies in its distinctive self-attention mechanism, which adeptly captures intricate dependencies among vectors at any position within the input sequence, transcending the limitations of traditional sequential models in processing temporal information. Not only does the Transformer facilitate efficient parallel training, boosting model training speed to unprecedented levels, but it also effectively preserves the temporal characteristics of sequences through deep learning capabilities, ensuring high-precision predictions. Since its inception, the Transformer model has swiftly garnered widespread attention from researchers due to its remarkable performance and flexibility, particularly demonstrating immense potential in the field of building energy load forecasting. Figure 15 shows a Transformer model used for building load forecasting [171].  
Figure 15: The Transformer model architecture [171].  
As shown in Figure 15, a transformer model specifically designed for predicting the cooling loads of commercial buildings has been developed. This model harnesses the power of the self-attention mechanism to precisely model long-range dependencies between input and output sequences, effectively capturing and retaining crucial information from long-term time series, thereby providing a more comprehensive and insightful basis for cooling load predictions [172].  
[PERSON] et al. [173] have further broadened the application scope of the transformer by introducing it into the realm of multi-level building energy forecasting. They compared various strategies, including seven deep learning methods, with the temporal fusion transformer (TFT) model emerging as the standout performer, exhibiting exceptional capabilities in both point and probabilistic forecasting. Through empirical studies conducted on two different building types (a Swedish city museum and a theater), they validated the high predictability of their developed model across diverse energy consumption scenarios. [PERSON] et al. [174] proposed an innovative multi-task energy load forecasting model based on the Transformer architecture. This model employs a multi-decoder framework that utilizes multi-head attention mechanisms to focus on the encoder's output representations at various levels. This approach significantly enhances both the accuracy and efficiency of simultaneous predictions for multiple energy loads.  
Furthermore, [PERSON] et al. [175] have contributed by proposing an interpretable deep learning model that integrates LSTM and self-attention mechanisms. By introducing a dual attention mechanism based on hidden states and features, they have enhanced the transparency and credibility of the model in building energy consumption forecasting. They validated the effectiveness and practicality of their model through a case study involving an office building.  
Collectively, these studies have not only deepened the application of the Transformer model in the field of building energy but also provided robust technical support for future intelligent energy management and optimization.