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Open Access

Personalized Federated Learning for Individual Consumer Load Forecasting

Yi Wang1,2 ( ), Ning Gao1,2, Gabriela Hug3
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China
ETH Zurich, Zurich, Switzerland
Power Systems Laboratory, ETH Zurich, 8092 Zurich, Switzerland
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Abstract

Installation of smart meters enables electricity retailers or consumers to implement individual load forecasting for demand response. An individual load forecasting model can be trained either on each consumer's own smart meter data or the smart meter data of multiple consumers. The former practice may suffer from overfitting if a complex model is trained because the dataset is limited; the latter practice cannot protect the privacy of individual consumers. This paper tackles the dilemma by proposing a personalized federated approach for individual consumer load forecasting. Specifically, a group of consumers first jointly train a federated forecasting model on the shared smart meter data pool, and then each consumer personalizes the federated forecasting model on their own data. Comprehensive case studies are conducted on an open dataset of 100 households. Results verify the proposed method can enhance forecasting accuracy by making full use of data from other consumers with privacy protection.

Keywords

privacy-preservingpersonalizationFederated learningload forecastingsmart meter

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CSEE Journal of Power and Energy Systems
Volume 9 Issue 1,
January 2023
Pages 326-330
DOI: 10.17775/CSEEJPES.2021.07350
Cite this article:
Wang Y, Gao N, Hug G. Personalized Federated Learning for Individual Consumer Load Forecasting. CSEE Journal of Power and Energy Systems, 2023, 9(1): 326-330. https://doi.org/10.17775/CSEEJPES.2021.07350

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Received: 30 September 2020
Revised: 09 January 2022
Accepted: 08 February 2022
Published: 06 May 2022
© 2021 CSEE
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