## 1 Introduction
In the data collection phase, real operational data of the building is obtained for the establishment of the data model [14]; in the data preprocessing phase, the data are cleaned, the correlation between features and energy consumption is analyzed, the main features are screened, and the feature dimensions are unified [15]; during the model training and testing phase, regression, neural network and other techniques are used to construct the data model, and the optimal hyper-parameters of the model are obtained through testing [16]; during the model evaluation and revision phase, universal indicators such as mean absolute percentage error (MAPE), root mean square error (RMSE), etc. are used to evaluate the model results [17]. Then, apply the model to real online operations, and the model parameters are continuously optimized based on the feedback results. The data-driven approach is more reflective of the operational status of building energy systems than physical modeling methods. The data model can be trained online and continuously revised and optimized, suitable for predicting energy consumption trends and future energy demand, which is beneficial for building managers to allocate energy usage reasonably, optimize energy structure, and make wise decisions. In the current context of rapid technological development, a series of emerging technologies are having a transformative impact on the field of building energy consumption prediction. The rise of the Internet of Things (IoT) technology enables the interconnection of various devices within buildings, allowing for the real-time collection of massive amounts of energy data. This provides a richer and more accurate data source for data driven methods, greatly enhancing the reliability and accuracy of energy consumption prediction models. With its powerful data storage, processing, and analysis capabilities, big data technology can conduct in-depth mining of vast amounts of building energy consumption data, uncovering potential energy consumption patterns and laws, thus assisting in optimizing prediction models. In addition, the continuous innovation in the field of artificial intelligence, especially the continuous evolution of deep-learning algorithms, makes it possible to build more complex and intelligent energy consumption prediction models. For example, models based on recurrent neural networks (RNNs) and their variant long short-term memory networks (LSTMs) can effectively process time-series data and capture the dynamic characteristics of building energy consumption changes over time, demonstrating excellent performance in both short-term and long-term energy consumption predictions. Generative adversarial networks (GANs) have also begun to be applied in the field of building energy consumption prediction. By generating simulated data to expand the training dataset, the generalization ability of the model is improved. The integrated application of these emerging technologies is expected to reshape the technological landscape of building energy consumption prediction and provide strong support for more efficient and accurate energy consumption prediction. In the current context of global sustainable development, research in the field of building energy consumption prediction is of vital significance for alleviating the energy crisis and reducing carbon emissions. Although previous studies have achieved certain results, with the rapid development of technology and the increasingly severe energy situation, there are still many gaps that urgently need to be filled. The core motivation for writing this review paper is as follows. On the one hand, the continuous innovation of building energy consumption prediction technologies makes it necessary for us to sort out the latest research progress to meet the changing energy demands and the needs of building operation and management.