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

2020/12/14 by C. C. Briggs, Briggs, Christopher, Zhong Fan +2 · 1 citation
Engineering · #Computers and Society (cs.CY) #Electricity Theft Detection Techniques #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Energy Management

paper · pdf · doi:10.48550/arxiv.2012.07449

openalex publication_date 2020/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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