The Influence of Differential Privacy on Short Term Electric Load Forecasting

TL;DR

This paper explores how differential privacy impacts short-term electric load forecasting, revealing its potential to enable privacy-preserving data sharing that still supports effective energy load predictions.

Contribution

It demonstrates the utility of differential privacy in load forecasting, highlighting differences across methods and showing feasible privacy levels for households.

Findings

Linear regression provides the best utility among tested methods.

Energy providers can achieve good forecasting utility with differential privacy.

Households can participate with re-identification risk below 60%.

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 re-identification risk < 60%, only 10% over random guessing.