Efficient community building energy load forecasting through federated hypernetwork

Building energy consumption grows rapidly with modern urbanization while the buildings' sensor data also increases explosively. Improving energy utilization of community buildings is critical for sustainable development and global climate challenge. However, the data isolation across buildings' privacy management prevents largescale machine learning model training, which may reduce the prediction accuracy due to lack of data. Federated building energy learning supports distributed learning through model sharing so that data privacy is mitigated. In federated learning, model-sharing brings a new concern about network resource limitation. Deep learning model transfers across multiple buildings would cause network ingestion and incur high latency of federated training. To improve the efficiency of federated training with fewer resources, a new federated learning algorithm is proposed with a new deep learning model design. The deep learning model memory usage is reduced by 80% while energy load forecasting accuracy is still comparable to the state-of-the-art methods.