Efficient community building energy load forecasting through federated hypernetwork

Rui Wang; Rakiba Rayhana; Ling Bai; Zheng Liu

doi:10.1117/12.3012011

9 May 2024 Efficient community building energy load forecasting through federated hypernetwork

Rui Wang, Rakiba Rayhana, Ling Bai, Zheng Liu

Proceedings Volume 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II; 1295207 (2024) https://doi.org/10.1117/12.3012011
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rui Wang, Rakiba Rayhana, Ling Bai, and Zheng Liu "Efficient community building energy load forecasting through federated hypernetwork", Proc. SPIE 12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II, 1295207 (9 May 2024); https://doi.org/10.1117/12.3012011

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