Briggs, C ORCID: https://orcid.org/0000-0003-2069-3484, Fan, Z ORCID: https://orcid.org/0000-0002-5575-1536 and Andras, P ORCID: https://orcid.org/0000-0002-9321-3296 Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Tackling Climate Change with Machine Learning. (Unpublished)

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Abstract

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers' raw energy consumption data.

Item Type: Article
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Related URLs:
Depositing User: Symplectic
Date Deposited: 08 Jun 2021 12:15
Last Modified: 08 Jun 2021 12:15
URI: https://eprints.keele.ac.uk/id/eprint/9683

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