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Privacy-preserving federated learning for residential short-term load forecasting

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  • Fernández, Joaquín Delgado
  • Menci, Sergio Potenciano
  • Lee, Chul Min
  • Rieger, Alexander
  • Fridgen, Gilbert

Abstract

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.

Suggested Citation

  • Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011722
    DOI: 10.1016/j.apenergy.2022.119915
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    Cited by:

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    3. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    4. Harshit Gupta & Piyush Agarwal & Kartik Gupta & Suhana Baliarsingh & O. P. Vyas & Antonio Puliafito, 2023. "FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid," Energies, MDPI, vol. 16(24), pages 1-21, December.
    5. Chen, Bingyang & Zeng, Xingjie & Zhang, Weishan & Fan, Lulu & Cao, Shaohua & Zhou, Jiehan, 2023. "Knowledge sharing-based multi-block federated learning for few-shot oil layer identification," Energy, Elsevier, vol. 283(C).
    6. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    7. Veerasamy, Veerapandiyan & Hu, Zhijian & Qiu, Haifeng & Murshid, Shadab & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2024. "Blockchain-enabled peer-to-peer energy trading and resilient control of microgrids," Applied Energy, Elsevier, vol. 353(PA).
    8. Liu, Yixing & Liu, Bo & Guo, Xiaoyu & Xu, Yiqiao & Ding, Zhengtao, 2023. "Household profile identification for retailers based on personalized federated learning," Energy, Elsevier, vol. 275(C).
    9. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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