The wide availability of massive building operational data has motivated the development of advanced data-driven methods for building energy predictions. Existing data-driven prediction methods are typically customized for individual buildings and their performance are highly influenced by the training data amount and quality. In practice, buildings may only possess limited measurements due to the lack of advanced monitoring systems or data accumulation time. As a result, existing data-driven approaches may not present sufficient values for practical applications. A novel solution can be developed based on transfer learning, which utilizes the knowledge learnt from well-measured buildings to facilitate prediction tasks in other buildings. However, the potentials of transfer learning-based methods for building energy predictions have not been systematically examined. To address this research gap, a transfer learning-based methodology is proposed for 24-h ahead building energy demand predictions. Experiments have been designed to investigate the potentials of transfer learning in different scenarios with different implementation strategies. Statistical assessments have been performed to validate the value of transfer learning in short-term building energy predictions. Compared with standalone models, the transfer learning-based methodology could reduce approximately 15% to 78% of prediction errors. The research outcomes are useful for developing advanced transfer learning-based methods for typical tasks in building energy management. The insights obtained can help the building industry to fully realize the value of existing building data resources and advanced data analytics.

大量的建筑运营数据的广泛可用性推动了先进的数据驱动方法进行建筑能耗预测的发展。现有的数据驱动的预测方法通常是针对单个建筑物定制的，其性能在很大程度上受训练数据量和质量的影响。实际上，由于缺乏先进的监控系统或数据积累时间，建筑物只能具有有限的测量值。结果，现有的数据驱动方法可能无法为实际应用提供足够的价值。可以基于转移学习来开发一种新颖的解决方案，该学习利用从测量良好的建筑物中学到的知识来促进其他建筑物的预测任务。然而，基于传递学习的方法进行建筑能耗预测的潜力尚未得到系统地检查。为了解决这一研究空白，提出了一种基于转移学习的方法来提前24小时预测建筑物的能源需求。已经设计了实验来调查在不同方案和不同实施策略下转移学习的潜力。已经进行了统计评估，以验证转移学习在短期建筑能耗预测中的价值。与独立模型相比，基于迁移学习的方法可以减少约15％至78％的预测误差。研究结果对于开发用于建筑能源管理中典型任务的基于高级迁移学习的方法很有用。