Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings. However, it is not recommended to directly use the operational data without protection due to the risk of leaking occupants' privacy. To address this problem, this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking. It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data. An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data. The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building Data Genome Project. The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time. On average, the federated model achieves a 25.4% decrease in CV-RMSE when the target building has limited operational data. Even if the target building has no operational data, the federated model still achieves acceptable accuracy (CV-RMSE is 22.2%). Meanwhile, the training time of the federated model is 90% less than that of the standalone model. The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management. The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.