## 5 Discussion
### Opportunities for Further Works
This may involve the development of data cleaning, matching, transformation, and fusion algorithms. 5. Edge computing has important applications in building energy consumption prediction. Edge computing is capable of performing data processing at devices close to data sources or at the network edge, reducing data transmission latency and increasing data processing efficiency. In the context of building energy consumption prediction, by deploying edge-computing nodes on intelligent devices within buildings, the locally-collected energy-consumption data can be analyzed in real-time. This not only enables a rapid response to real-time changes in building energy consumption and the timely adjustment of prediction-model parameters but also eases the burden on the cloud-computing center and safeguards data privacy. For example, edge computing can perform preliminary processing on the high-frequency energy-consumption data generated in real-time by smart meters. After extracting key features, the data are then transmitted to the cloud for in-depth analysis, thus enhancing the timeliness and accuracy of the overall prediction. 6. Blockchain technology plays a facilitating role in the management of building energy consumption data. Blockchain, with its characteristics of decentralization, immutability, and traceability, provides new ideas for the management of building energy consumption data. In the field of building energy-consumption prediction, data from different sources may have trust-related issues, and blockchain can ensure data authenticity and integrity. For instance, through blockchain technology, the energy-consumption data collected from building management systems, smart meters, and other devices is recorded. The entry of each data item requires verification by multiple nodes, ensuring that the data cannot be maliciously tampered with. This helps to improve the credibility of data when integrating multi-source data, laying a foundation for constructing a more reliable energy-consumption prediction model. Meanwhile, the smart-contract function of blockchain can also be used to automate the sharing and trading of energy-consumption data, promoting data circulation and rational utilization. 7. There is potential to be tapped for crowdsourced data in building energy consumption prediction. Crowdsourced data, sourced from a large number of users, has advantages such as a large volume of data and wide coverage. In building energy-consumptionprediction, information about building usage habits and users' perception of environmental comfort can be collected through crowdsourcing platforms. These data can supplement the deficiencies of data collected by traditional sensors and reveal the influencing factors of building energy consumption from the perspective of user behavior. For example, users can upload information such as their indoor temperature-adjustment preferences and the frequency of electrical-appliance use in the building through a mobile application. Combining these crowdsourced data with the actual energy-consumption data of the building can lead to a more comprehensive understanding of the complex patterns of building energy consumption, thereby optimizing the energy-consumption prediction model and improving the accuracy and comprehensiveness of the prediction. 8. Interpretability and transparency in building energy consumption forecasting. The pervasive use of deep learning models for building energy consumption forecasting introduces challenges related to interpretability and transparency due to their complexity and \"black box\" nature [184]. Future research can explore methods to enhance the interpretability of deep learning models, making their predictions more transparent and trustworthy. For instance, investigations could focus on employing interpretable machine learning techniques such as LIME and SHAP to elucidate deep learning model predictions, thereby aiding building managers in understanding the decision-making processes of these models.