## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Data Acquisition  
To accurately predict future energy demand, it is essential to conduct in-depth analysis and extensive collection of historical and real-time data. The aggregation and analysis of these data can uncover the fluctuation patterns of building loads, thereby enhancing  
Figure 12: Building energy consumption prediction framework. the accuracy of predictions. There are three main ways to obtain these data: direct field collection, retrieval of open datasets, and software simulation generation. Field collection captures building loads and related indicators through precision sensors. In terms of data quality, this method enables the acquisition of high-quality data with high resolution that closely matches the actual situation of specific buildings, accurately reflecting the real-time dynamics of building energy consumption. However, it is extremely costly, involving not only the procurement and installation costs of sensors but also long-term maintenance expenses. Moreover, due to the professionalism of data collection and the limitations of specific locations, the public accessibility is extremely low. During the data collection process, sensor failures or signal transmission problems occur frequently, which necessitate data cleaning, such as filling in missing values and noise reduction, to ensure the availability and accuracy of the data. As countries implement energy data openness policies to promote the energy efficiency of buildings, the building energy consumption data publicly available from governments and organizations has become an important resource for research. Take the open dataset of urban building energy from 33 cities across 8 countries compiled by [PERSON] and her team as an example. This dataset covers 13 types of variables, providing valuable references for urban building energy consumption modeling and the development of energy policies [28]. In terms of data quality, such open datasets have a certain degree of standardization and authority after being organized and reviewed. However, in terms of accessibility, due to factors such as the increased difficulty of data collection and privacy protection, the proportion of high-frequency data in open datasets is relatively low. This poses a significant challenge to the short-term building energy consumption prediction work that requires high-frequency data to capture the details of short-term energy consumption changes. To make up for this deficiency, researchers also use software simulation methods to generate building energy consumption data. Mature software such as EnergyPlus, ESP-r, and TRNSYS can simulate and generate data by inputting the physical parameters and specific equations of buildings. These simulated data play an important role in the initial stage of model training and can be used to initially construct the model framework. However, in terms of data quality, the simulated data are generated based on theoretical models and assumptions after all, and there is a certain deviation from the real-world situation. Subsequently, real-world data are required to iteratively optimize the model to improve its accuracy and reliability. In terms of accessibility, relevant software generally has corresponding acquisition channels. As long as researchers have the corresponding software usage knowledge and hardware conditions, they can relatively easily acquire and use this software to generate data. Despite the diverse methods of acquiring building energy consumption data, the issues of inconsistent data quality and scarcity of high-frequency data remain challenging. In the context of insufficient data, the application of transfer learning in building energy consumption prediction has been increasing. [PERSON]'s online transfer learning framework, for instance, has significantly improved the temperature prediction accuracy of new buildings using only data from the previous few weeks [132].