Transferability investigation of a Sim2Real deep transfer learning framework for cross-building energy prediction

Abstract

Data-driven methods generally require large-scale historical data volume to obtain a better building energy prediction performance. Transfer learning methods have been widely used to address this issue by utilizing knowledge learned from similar tasks. However, most studies on building energy prediction are mainly focused on the effect of a single transfer learning model. There are few studies to systematically investigate the effect of building types, climate zones and data volume on energy prediction performance by using different transfer learning models with the auxiliary simulation data. This study proposes a Sim2Real transfer learning framework using simulation datasets for building energy prediction to fill this research gap. Three different experiment scenarios are conducted to investigate the effect of different building types, climate zones and data volume on the prediction performance from the perspective of simulation to reality by using the proposed Sim2Real transfer learning framework. Extensive experiment cases demonstrate that the proposed Sim2Real transfer learning framework can effectively enhance the MAPE by 1.06%∼29.05% with proper transfer learning models compared with the baseline model LSTM. In addition, transfer learning strategies are not necessary when the training data volume of target building is over five weeks. The proposed Sim2Real transfer learning framework can provide the bridge to enhance the building energy prediction performance from the perspective of simulation to reality.