Forecasting Anonymized Electricity Load Profiles

TL;DR

This paper investigates how anonymized, microaggregated electricity load data can be effectively forecasted, demonstrating that privacy-preserving techniques do not significantly reduce forecasting accuracy, thus enabling privacy-compliant smart metering applications.

Contribution

It shows that microaggregation-based anonymization allows accurate load forecasting at an aggregated level, balancing privacy and utility in energy data.

Findings

Microaggregation preserves forecasting accuracy at aggregated levels.

Anonymized load profiles can be effectively forecasted without privacy loss.

Privacy-preserving data practices are compatible with smart metering applications.

Abstract

In the evolving landscape of data privacy, the anonymization of electric load profiles has become a critical issue, especially with the enforcement of the General Data Protection Regulation (GDPR) in Europe. These electric load profiles, which are essential datasets in the energy industry, are classified as personal behavioral data, necessitating stringent protective measures. This article explores the implications of this classification, the importance of data anonymization, and the potential of forecasting using microaggregated data. The findings underscore that effective anonymization techniques, such as microaggregation, do not compromise the performance of forecasting models under certain conditions (i.e., forecasting aggregated). In such an aggregated level, microaggregated data maintains high levels of utility, with minimal impact on forecasting accuracy. The implications for the energy sector are profound, suggesting that privacy-preserving data practices can be integrated into smart metering technology applications without hindering their effectiveness.