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Federated Learning for Short-term Residential Load Forecasting

Briggs, Christopher; Fan, Zhong; Andras, Peter


Christopher Briggs

Zhong Fan

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Prof Peter Andras
Dean of School of Computing Engineering and the Built Environment


Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters will prove a vital component to facilitate these forecasting tasks. However, smart meter adoption is low among privacy-conscious consumers that fear intrusion upon their fine-grained consumption data. In this work we propose and explore a federated learning (FL) based approach for training forecasting models in a distributed, collaborative manner whilst retaining the privacy of the underlying data. We compare two approaches: FL, and a clustered variant, FL+HC against a non-private, centralised learning approach and a fully private, localised learning approach. Within these approaches, we measure model performance using RMSE and computational efficiency. In addition, we suggest the FL strategies are followed by a personalisation step and show that model performance can be improved by doing so. We show that FL+HC followed by personalisation can achieve a ~5% improvement in model performance with a ~10x reduction in computation compared to localised learning. Finally we provide advice on private aggregation of predictions for building a private end-to-end load forecasting application.


Briggs, C., Fan, Z., & Andras, P. (2022). Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, 9, 573-583.

Journal Article Type Article
Acceptance Date Sep 7, 2022
Online Publication Date Sep 12, 2022
Publication Date 2022
Deposit Date Nov 15, 2022
Publicly Available Date Feb 15, 2023
Journal IEEE Open Access Journal of Power and Energy
Print ISSN 2687-7910
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 9
Pages 573-583
Keywords federated learning, load forecasting, distributed machine learning, deep learning, data privacy, internet-of-things
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