## 5 Discussion
### Challenges  
1. In practical applications, data-related issues pose numerous challenges to building energy consumption prediction. In terms of data quality and integrity, data often suffers from noise, missing values, and inconsistent formats. For example, sensor failures can lead to data loss, and differences in device data formats impede integration. There is an urgent need for efficient data cleaning and preprocessing techniques. Data privacy and security are also crucial. As the data encompasses more information, preventing data leakage and safeguarding user privacy while achieving effective energy consumption prediction during data sharing and transmission is a difficult problem. For instance, when cloud platforms process data, strict control over encryption and access rights is required. Moreover, multi-source data integration is extremely challenging. Data from different sources vary greatly in time resolution, spatial granularity, and quality. For example, meteorological data and smart meter data have different time scales. How to match and integrate them to improve prediction accuracy remains to be further studied.
2. Model-related challenges restrict the practical application of building energy consumption prediction models. Regarding model complexity and interpretability, although deep-learning models are accurate in prediction, their structures are complex, and they are in a \"black-box\" state. Building managers and energy professionals find it difficult to understand their decision-making processes. For example, the Transformer model has poor interpretability when used for commercial building cooling load prediction. Therefore, developing interpretable deep-learning models or combining interpretable technologies is an important direction. The adaptability and generalization ability of models are insufficient. Different buildings vary in structure, usage patterns, and environments, and existing models struggle to quickly adapt to different scenarios. For example, a model for northern residential buildings cannot be directly applied to southern commercial buildings. The real-time performance and dynamic adjustment of models also urgently need improvement. The dynamic nature of the energy system requires models to be updated rapidly based on real-time data to respond to changes in building usage patterns and the environment. For example, when there is a sudden increase in personnel activities leading to a rise in energy consumption, the model needs to adjust the prediction results in real-time.
3. Multiple factors in the actual application environment pose challenges to building energy consumption prediction. The building operation environment is constantly changing. Equipment aging, renovation, and changes in user behavior affect energy consumption characteristics, and existing models have difficulty capturing these in real-time. For example, the prediction accuracy of the model is easily affected when energy-saving lighting equipment is replaced or air-conditioning usage habits change. Policy, regulatory, and market factors cannot be ignored. Changes in energy policies and regulations and fluctuations in energy market prices can change building energy consumption patterns, but these are often not fully considered in models. For example, when an energy-saving subsidy policy is introduced, it needs to be incorporated into the model to conform to reality. In addition, the coordinated optimization of multiple systems is difficult. Building energy consumption prediction needs to be coordinated with smart grids and regional energy systems, but the coordination mechanisms among systems are complex and involve multiple stakeholders. For example, how to adjust power consumption strategies reasonably based on building energy consumption predictions during peak grid loads to achieve a win-win situation is a key challenge.