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Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

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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.

Conference Name NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning
Start Date Dec 6, 2021
End Date Dec 12, 2021
Acceptance Date Dec 6, 2021
Publication Date Dec 6, 2021
Publisher URL https://www.climatechange.ai/papers/neurips2020/78.html

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