## 5 Discussion
### Comparative Analysis and Application Considerations of Prediction Models
However, LSTM has a high computational complexity, and its complex model structure leads to high requirements for computing resources in training and prediction. Moreover, the training is difficult and requires a large amount of data and time for parameter tuning. It is suitable for long-term and accurate prediction of building energy consumption time series, especially for buildings where energy consumption is greatly affected by time factors and long-term trends and seasonal changes need to be captured, such as energy consumption prediction for large public buildings or industrial buildings. Based on the unique self-attention mechanism, the Transformer can cleverly capture the complex dependencies between vectors at any position in the input sequence, breaking through the limitations of traditional sequential models in processing time information. The efficient parallel training greatly improves the training speed, showing great potential in the field of building energy consumption load prediction. It has been successfully applied to the prediction of cooling loads in commercial buildings, energy consumption in multi-story buildings, etc., and has excellent performance in point prediction and probability prediction. However, it has high requirements for the amount of data. When the data are insufficient, it is difficult to give full play to its advantages and may even have fitting problems. Moreover, the model is complex and belongs to a black-box model, making it difficult to understand and interpret the decision-making process. It is suitable for scenarios with large-scale and high-quality building energy consumption data and high requirements for prediction accuracy and computational efficiency, such as the overall prediction of urban building energy consumption or the fine-grained management prediction tasks of large building clusters. The KNN algorithm in building energy consumption prediction has a simple and intuitive principle. There is no need for a complex training process, and the model can be updated by adding new samples, which is easy to understand and operate. However, this algorithm needs to traverse the entire training dataset to calculate distances during prediction, resulting in high computational complexity. In large-scale datasets, the prediction speed is slow, and the selection of the K value has a great impact on the prediction result. If the K value is too small, the model is sensitive to noise, and if it is too large, the model becomes fuzzy. It is necessary to carefully select the K value through methods such as cross-validation. It is suitable for scenarios with a small dataset scale, low requirements for real-time performance, and the expectation of quickly obtaining simple prediction results, such as the preliminary exploration of energy consumption prediction for small buildings with limited data. CNN was initially used for image recognition and now has emerged in the field of building energy consumption prediction. Through components such as convolutional layers and pooling layers, it can automatically learn data features without manual design, effectively mining the local patterns in building energy consumption data, such as the energy consumption change patterns in different time periods. It also has translation invariance, has good adaptability to local feature changes, and can accurately capture data patterns. However, CNN has a limited ability to handle long-term dependencies in time series, which is weaker than specialized time series models. Moreover, in order to be better applied to building energy consumption prediction, it often needs to be combined with other models such as LSTM, increasing the complexity of model structure adjustment.