## 4 Data-Driven Framework for Building Energy Consumption Prediction
### Feature Engineering  
Feature engineering is a crucial component in data-driven methodologies [144]. By preprocessing and transforming raw data, it constructs features that can effectively capture the essence of the problem, thereby enhancing the predictive performance of models [145]. In the context of building energy consumption prediction, feature engineering holds paramount importance due to the intricate interplay of multi-dimensional and multi-level factors influencing building energy consumption, including meteorological information, indoor environments, occupancy patterns, building characteristics, and socio-economic information [146]. The influencing factors are summarized in Table 2.  
Meteorological information [147; 148] encompasses outdoor temperature, humidity, wind speed, solar radiation, rainfall, air pressure, and other factors, all of which directly impact the energy demand of buildings. Indoor environmental information [149; 150], on the other hand, comprises set temperature, indoor temperature, humidity, carbon dioxide concentration, and so forth, influencing both user comfort and building energy consumption. Occupancy-related data [151; 152], such as the number of occupants and types of activities, affect the internal load of buildings. Time indices [153], including date, day of the week, time of day, and holidays, reflect occupancy patterns and activity rhythms. Building characteristic data encompasses compactness, surface area, wall area, roof area, height, orientation, glass area, thermal transmittance coefficient, and other factors, all of which influence the specific energy demand characteristics of buildings [154]. Socio-economic information [155], including income levels, electricity prices, GDP, population, and the like, shapes users' energy consumption behaviors and, consequently, building energy consumption. Lastly, historical data, comprising historical meteorological data, historical energy consumption data, and the like, reflect the load characteristics of buildings. The prevalent feature extraction methods include [146]:  
1. Variable ranking is employed, which involves identifying the most relevant features to the target variable through techniques such as correlation analysis. Notable examples of this approach include the use of the Pearson correlation coefficient and the Spearman rank correlation coefficient. 2. There are filtering and wrapper methods, which assess the predictive performance of individual features or subsets of features to determine the optimal feature set. Principal component analysis (PCA) and random forest (RF) are among the commonly used techniques within this category. 3. Embedded methods represent another significant approach, wherein feature selection is seamlessly integrated into the learning algorithm itself. Lasso regression is a prime example, as it inherently performs feature selection as part of its regression process. 4. Autoencoders (AEs) utilize deep learning techniques to learn nonlinear representations of the input data, thereby effectively extracting useful features. This method leverages the power of neural networks to uncover complex patterns within the data. Feature engineering plays a pivotal role in building energy consumption prediction. By selecting appropriate feature types and employing effective feature extraction methods, it can significantly enhance the predictive performance of models, reduce their complexity, and improve their interpretability. In the future, as data collection and analysis technologies continue to evolve, feature engineering will become even more crucial, providing more potent tools for building energy consumption predictions. Through the above feature engineering, a series of factors that have a significant impact on building energy consumption were screened out. These factors constitute