## 1 Introduction  
The energy demand for buildings is increasing globally and gradually becoming a major contributor to global energy consumption. The report from the International Energy Agency shows that the energy consumption of the construction industry accounts for over 33% of the global total energy consumption [1, 2]. Due to the continuous growth of the population and the acceleration of urbanization, the energy required for buildings is constantly increasing. In the United States and the European Union, the energy demand of the construction industry accounts for over 40% of all energy consumption [3, 4]. In China, building energy consumption contributes 20% of total energy consumption, and the carbon dioxide emissions of the construction industry in these three regions all exceed 30% [5]. Therefore, reducing building energy consumption is the primary issue that countries around the world need to face in energy conservation and emission reduction. Building energy consumption prediction is one of the most effective means of improving energy efficiency in existing technologies. Accurate energy consumption prediction is the foundation of energy network scheduling, which helps to achieve matching and regulation of energy supply and demand, ensures the low-cost and efficient operation of building energy systems, and provides auxiliary decision-making and data support for building managers to formulate optimal operational strategies [6; 7]. Physical modeling methods and data-driven methods are the two main methods for predicting building energy consumption [8; 9]. The physical modeling method models the characteristics of the building itself, which can represent the building characteristics of different parts, materials, and uses, and considers the interaction of building elements. Through physical models, building energy consumption prediction can conduct multi-dimensional and multi-dimensional performance analysis and optimization of buildings during the design and operation stages, including building structure, exterior walls, lighting, fire ventilation, heating and ventilation, wind and solar energy utilization, and other aspects. Building energy consumption simulation software such as Energy-plus and e-Quest have become very mature in physical modeling and can simulate multiple types of building energy consumption systems [10]. However, the real-time interaction between residents, energy systems, and the environment is very complex, and it is difficult for physical methods to reflect the real interactions, resulting in unsatisfactory accuracy of physical modeling methods [11]. Although some researchers have proposed optimization methods, such as Bayesian estimation, to improve accuracy [12], these methods may increase costs and make it difficult to achieve online applications. Therefore, physical modeling methods may be more suitable for estimation during the architectural design phase rather than accurate online prediction [13]. The data-driven approach utilizes historical operational data of buildings to analyze the main factors affecting energy consumption and constructs data models using machine learning or deep learning techniques to simulate the real process of building energy consumption. This type of method usually consists of four steps: data collection, data preprocessing, model training and testing, and model evaluation and correction [8].