Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters
(2021)
Presentation / Conference
Briggs, C., Fan, Z., & Andras, P. (2021, December). Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters. Poster presented at NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning
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 ho... Read More about Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters.