Statistical investigations of transfer learning-based methodology for short-term building energy predictions

Abstract

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.