## 6 Conclusions  
As society's ongoing concern over the scarcity of natural resources and the degradation of the ecological environment intensifies, the construction industry is urgently seeking groundbreaking innovative technologies to accurately predict and optimize building energy consumption, thereby significantly enhancing energy efficiency. Among existing technological systems, building energy consumption forecasting emerges as an efficient strategy that is crucial not only for maintaining the stable operation of building energy systems but also for providing solid data support for managers to formulate scientific and rational operational strategies. This paper aims to comprehensively review the latest advancements in data-driven building energy consumption forecasting methods over recent years. Based on different prediction time scales, building energy consumption prediction is segmented into very-short-term, short-term, medium-term, and long-term load forecasting. Each category carries specific research values and practical implications, which are thoroughly analyzed and explained with corresponding forecasting methodologies. Furthermore, for varying building types such as industrial, school, office, commercial, and residential buildings, their unique energy consumption characteristics are meticulously analyzed to uncover their inherent patterns. Additionally, from the perspective of energy types, this paper summarizes diverse energy forecasting strategies, offering insights for cross-domain applications. A comprehensive framework for building energy consumption prediction techniques is constructed, with the forecasting process as the main thread. It elaborates on data acquisition strategies, the application of feature selection methods, the construction of data-driven prediction models, and comprehensive considerations for model evaluation and application practices. Multiple avenues for data acquisition are covered, including direct on-site collection, retrieval from public datasets, and software simulation generation, while also cautioning against potential data quality issues. In particular, seven key factors influencing building load, including meteorological information, indoor environmental information, occupancy data, temporal dimensions, building characteristics, socio-economic backgrounds, and historical energy consumption data, along with four efficient feature extraction techniques, are introduced in detail. At the prediction model level, five mainstream data-driven models, including polynomial regression, SVR, MLP, LSTM, and Transformers, are analyzed in depth, revealing their unique strengths and applicable scenarios in handling complex energy consumption data. Meanwhile, by summarizing five core model evaluation metrics MAE, MAPE, MSE, RMSE, and R-squared, a quantitative basis is provided for objectively assessing model performance. However, this study also has certain limitations. In terms of data, although multiple data acquisition methods are mentioned, direct on-site data collection often faces challenges such as insufficient data volume, high data collection costs, and data privacy protection. Moreover, public datasets may have problems such as inconsistent data formats and limited data coverage, which may affect the accuracy and generalization ability of model training. At the model level, the five mainstream data-driven models analyzed, although having advantages in their respective applicable scenarios, may experience a significant decline in prediction accuracy under special circumstances such as extreme weather conditions or major changes in building structures. Additionally, the training process of these models usually requires a large amount of computational resources and time costs. In terms of application scenarios, the current research mainly focuses on common building types.