Scalable cloud-based transfer learning for building energy prediction from limited digital twin data

Data scarcity is one of the major barriers to using Machine Learning (ML) techniques for building energy predictions. Transfer Learning (TL) has proven to be an effective approach to address this issue. However, most TL approaches have not considered scalability, which is an important factor when using Digital Twin (DT) data for predictions. To bridge this gap, this research aims to develop a scalable TL approach for predicting building energy consumption from limited DT data using cloud computing. Two frameworks, using Microsoft Azure and Amazon AWS, are developed and implemented in a case study to demonstrate their applicability and practicality. The findings show that both frameworks possess unique strengths in terms of scalability and improving accuracy. Azure performed better in training time and resource efficiency, while AWS excelled in data preprocessing speed and prediction accuracy. This paper supports researchers and practitioners in developing a scalable model (i.e., cloud-based TL) for predicting building energy consumption with limited DT data. Future research can explore the cost of using cloud platforms for building energy prediction and expand the list of decision factors for platform selection.