## 1 Introduction
On the other hand, existing reviews mostly focus on traditional machine learning algorithms, lacking a comprehensive and in-depth summary of deep learning technologies that have emerged in the field of building energy consumption prediction in recent years. Especially in the past six years, deep learning has demonstrated great potential in aspects such as data processing and improvement of model accuracy, and its technical route has undergone tremendous changes. However, there is a severe shortage of relevant, comprehensive reviews. The work of this paper systematically integrates the latest progress of deep-learning-based building energy consumption prediction methods, breaking through the limitations of only focusing on traditional machine learning algorithms in the past. Through an in-depth analysis of the multi-dimensional building load classification system, it is possible to more accurately grasp the characteristics of building energy consumption in different scenarios, providing a solid foundation for the subsequent construction of data-driven models. In addition, taking the prediction steps as the main line, a comprehensive and scientific data-driven energy consumption prediction framework is innovatively established, elaborating on the cutting-edge technologies and methods in each link from data collection to model validation. This provides a new perspective and operation guide for future research, helping to improve the accuracy and reliability of building energy consumption prediction and contributing to the building industry's achievement of more efficient energy management and energy conservation and emission reduction goals. The main structure of this paper is shown in Figure 1. The paper is organized as follows. Section 2 provides a brief overview of the review articles on building energy consumption prediction in recent years. Section 3 delves into the multifaceted classification system of building load types. This system meticulously categorizes loads based on three key dimensions: time span, building type, and energy type, aiming to comprehensively cover and precisely identify load characteristics across diverse scenarios. Section 4 establishes a systematic, data-driven framework for energy consumption forecasting. It provides a thorough exposition of each critical stage within this framework, highlighting the prevalent techniques and methodologies employed in data collection, preprocessing, model development, training, and optimization, as well as result validation. This ensures the scientific rigor and accuracy of the forecasting process. Section 5 offers a forward-looking perspective on the future research directions in energy consumption forecasting and related fields. It identifies potential emerging trends, challenges, and corresponding strategies, thereby illuminating the path for future research endeavors and practical applications. Lastly, Section 6, serving as the conclusion, reviews the primary arguments and findings presented throughout the document. It underscores the significance of nuanced classification of building load types and innovative approaches to energy consumption forecasting. Furthermore, it summarizes the practical implications of the research findings in advancing energy efficiency and fostering the development of green buildings.