Performance assessment of cross office building energy prediction in the same region using the domain adversarial transfer learning strategy

Abstract

Precise and reliable building energy consumption prediction is of great significance for building energy management and future energy planning. To mitigate the adverse impact of data limitation on the performance of building energy consumption prediction model, a transfer learning strategy was introduced. However, the operation modes of different building energy systems are different, resulting in a large difference in the distribution of building energy consumption related data. In this paper, the relevant data of 25 buildings from the same region of the United States were selected. On this basis, two similarity calculation strategies are compared, and three similarity measurement methods were used to analyze the impact of different source buildings on the performance improvement of transfer learning models. The results show that choosing the appropriate source building can improve the transfer learning performance by up to 82.56% when compared to the no-transfer model (the baseline model). When higher similarity source buildings are selected, the corresponding average PIR is about 24.7% higher than the total average, which can make the transfer model performance improvement more obvious. This paper provides insights into the selection of source domains for transfer learning strategies under the same region.