Abstract
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to billing at the energy provider. However, exploitation is mostly limited to application of cryptographic security means between smart meters and energy providers. We illustrate along the use case of privacy preserving load forecasting that Differential Privacy is indeed a valuable addition that unlocks novel information flows for optimization. We show that (i) there are large differences in utility along three selected forecasting methods, (ii) energy providers can enjoy good utility especially under the linear regression benchmark model, and (iii) households can participate in privacy preserving load forecasting with an individual membership inference risk <60%, only 10% over random guessing.
Highlights
Smart metering data is said to be useful for improving the load forecasting task of energy providers (McDaniel and McLaughlin 2009; Li et al 2010; Ilicet al. 2013; Bao and Lu 2015)
As this conflicts with the privacy loss incurred by the acquisition of individual load profiles, we designed a differentially private metering process based on building blocks already proposed in previous works
Using three well-documented load forecasting approaches, we evaluate whether using smart metering data provides an actual benefit for the energy provider
Summary
Smart metering data is said to be useful for improving the load forecasting task of energy providers (McDaniel and McLaughlin 2009; Li et al 2010; Ilicet al. 2013; Bao and Lu 2015). Smart metering data is said to be useful for improving the load forecasting task of energy providers With more accurate forecasts, energy providers gain an advantage for trading and scheduling electricity production and consumption ahead of time. Forecasting errors have to be compensated by buying control energy for stable electric grid operation. The highly volatile control energy prices charged for this compensation can be painful for the energy providers. On the other hand, monitoring electrical load from individual households incurs violation of privacy, as private behavior patterns are reflected in the energy consumption (McDaniel and McLaughlin 2009; Molina-Markham et al 2010; Lisovich et al 2010). The amount of privacy violation varies depending on the monitoring time resolution of metering data (Eibl and Engel 2015). Using Differential Privacy (Dwork 2006) as privacy model, both time granularity and varying levels of the privacy parameters can be used to quantify and interpret the influence on privacy
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