## 5 Discussion  
### Opportunities for Further Works  
The data-driven approach is spearheading profound transformations in the field of building energy consumption prediction. This methodology not only delves deeply into the inherent logic and intricate mechanisms of energy consumption data but also provides a solid scientific foundation for precise prediction of building loads, optimization of energy allocation, and enhancement of energy efficiency levels [178, 179]. As such, it has emerged as a pivotal driving force propelling research and innovation in the field of building energy towards greater heights. Looking ahead, the research directions for building energy consumption prediction will become increasingly diversified and cutting-edge, encompassing the following:  
1. Multi-agent collaboration for energy load forecasting and optimization. Confronted with the complex energy systems composed of diverse building types within cities or industrial parks, the intricate interplay among various energy loads poses unprecedented challenges for accurately forecasting overall energy loads. The multi-agent collaboration approach, with its distinct advantages of distributed processing, adaptability, and efficient coordination, offers an innovative solution to this dilemma. By simulating the behavioral patterns of different buildings as independent agents, this method can capture and process the dynamic interactions among buildings and with the external environment, thereby achieving high-precision forecasting and intelligent optimization of the regional comprehensive energy loads, providing robust support for energy management decision-making. 2. Optimization and efficiency enhancement of deep learning model frameworks in building energy consumption prediction. Deep learning models, with their formidable data-fitting capabilities, have demonstrated immense potential in building energy consumption predictions. However, their high demands for training data volume and quality, as well as the substantial computational resources required, constitute bottlenecks in practical applications. Consequently, future research should concentrate on refining deep learning model frameworks, aiming to strike an optimal balance between computational efficiency and prediction accuracy with minimal computational resources. This encompasses strategies such as designing lightweight network structures, exploring efficient training algorithms, and optimizing data preprocessingprocedures, with the goal of maintaining or even enhancing prediction accuracy and timeliness while reducing resource consumption. 3. Exploration of generative pre-trained models in building energy consumption prediction. As large language models proliferate across industries, their robust semantic understanding and generation capabilities have sparked new insights into the realm of time-series analysis [180]. Particularly in building energy consumption prediction [181], the introduction and targeted fine-tuning of generative pre-trained models can harness their extensive context awareness and vast prior knowledge to uncover hidden complex patterns and trends within load data. This approach excels at capturing the nonlinear characteristics of load variations more precisely, enhancing the robustness and generalization capabilities of prediction models, thereby charting a promising new avenue for future research in building energy consumption prediction. 4. Multi-source data fusion for building energy consumption forecasting. With the advancement of Internet of Things (IoT) and smart sensor technologies, building energy consumption data can originate from multiple sources, including building management systems, smart meters, and weather stations [182]. These data sources vary in terms of temporal resolution, spatial granularity, and quality levels [183]. Future research should focus on effectively merging these diverse data sources to improve the accuracy and reliability of forecasts.