Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

Lingfeng Tang; Haipeng Xie; Xiaoyang Wang; Zhaohong Bie

doi:10.1016/j.apenergy.2023.120860

journal article May 01, 2023

Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

Lingfeng Tang Haipeng Xie

Xiaoyang Wang

Zhaohong Bie

Applied Energy Vol. 337 pp. 120860 · Elsevier BV

View at Publisher Save 10.1016/j.apenergy.2023.120860

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References

Details

Published: May 01, 2023
Vol/Issue: 337
Pages: 120860
License: View

Authors

School of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University Xi'an China; School of Future Technology Xi'an Jiaotong University Xi'an China

Funding

National Key Research and Development Program of China Award: 2021YFB2401300

Ministry of Science and Technology of the People's Republic of China

Cite This Article

Lingfeng Tang, Haipeng Xie, Xiaoyang Wang, et al. (2023). Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach. Applied Energy, 337, 120860. https://doi.org/10.1016/j.apenergy.2023.120860

Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

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