A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation

Lin, J; Ma, J; Zhu, J

A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation

Lin, J Ma, J Zhu, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Smart Grid, 2022, 13, (1), pp. 268-279
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Lin, J
dc.contributor.author	Ma, J
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2023-03-19T21:32:30Z
dc.date.available	2023-03-19T21:32:30Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Smart Grid, 2022, 13, (1), pp. 268-279
dc.identifier.issn	1949-3053
dc.identifier.issn	1949-3061
dc.identifier.uri	http://hdl.handle.net/10453/167582
dc.description.abstract	Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisible to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner assuming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM solar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to capture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant features can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Smart Grid
dc.relation.isbasedon	10.1109/TSG.2021.3115904
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering
dc.title	A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-19T21:32:29Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	1

Abstract:

Accurate estimation of residential solar photovoltaic (PV) generation is crucial for the power distribution and demand response program implementation. Currently, most distributed PVs are installed behind-the-meters (BTMs), and are thus invisible to the utilities. The existing methods separate the BTM solar generation from the available net load in a centralized manner assuming that all data are accessible to utilities. However, this can cause privacy issues, since the data are owned by different utilities and they may be unwilling to share their data. To this end, a novel method is proposed for disaggregating community-level BTM solar generation using a federated learning-based Bayesian neural network (FL-BNN), which can preserve the privacy of utilities. Specifically, a Bayesian neural network (BNN) is designed as the probabilistic energy disaggregation model with the ability to capture uncertainties. The BNN training process is extended into a decentralized manner based on the federated learning framework. To enable the model customized for each community, the layers of BNN are categorized into shallow and deep layers, and a layerwise parameter aggregation strategy is proposed to update the model. Both community-specific features and community-invariant features can be learned. The effectiveness of the proposed method is validated on a publicly available dataset.

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http://hdl.handle.net/10453/167582