An adaptive federated learning system for community building energy load forecasting and anomaly prediction

Abstract

Energy load forecasting is critical for sustainable building development and management. Although the energy data could be collected through Internet of Things (IoT) systems, it is a big challenge to train a large-scale machine learning model due to data isolation. Since the building energy data could reveal confidential information such as user behaviors and building operations, the privacy regulations would not allow central service to collect distributed data from data owners directly. This paper designs a secure federated data analytics system for forecasting community buildings' energy data load. A novel adaptive weight federated learning algorithm is proposed to handle the system faults frequently happening during networking operations. Moreover, a new deep learning model is re-invented to improve energy load forecasting performance. The experiments of the system are performed on an actual university campus dataset, and the results show the new federated algorithm improves the load forecasting accuracy and achieves the best load forecasting result. The new deep learning model improves the forecasting accuracy by almost 10% on error reduction under the same federated learning settings. To evaluate the load forecasting model's practical usefulness, an anomaly prediction pipeline is designed through the combination of gaussian mixture model and load forecasting model, which reveals the system's effectiveness at building energy management that 92% F1 score with 97% accuracy is achieved by the best model.