Forecasting Anonymized Electricity Load Profiles

2025-01-08

AI Summary

This study addresses the dual challenge of GDPR-compliant privacy protection and accurate load forecasting for smart meter data. We propose a microaggregation-based anonymization-prediction framework. Contrary to conventional anonymization methods that degrade time-series modeling accuracy, we systematically demonstrate—for the first time—that microaggregation, when applied at an appropriate granularity, preserves the statistical structure and dynamic characteristics of original load curves without significantly compromising prediction performance. Our method integrates microaggregation preprocessing with standard time-series models (e.g., LSTM and ARIMA) and introduces a quantitative privacy-utility evaluation framework. Experiments show that anonymized data incur less than 1.2% increase in prediction error—meeting industrial accuracy requirements. The work establishes a design paradigm wherein privacy protection need not entail utility loss, thereby providing both theoretical foundations and practical guidelines for compliant, high-fidelity smart metering applications.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Privacy Preservation
• Electricity Consumption Prediction
• Group Trend Analysis

Innovation

• Micro-aggregation
• Privacy-preserving
• Electricity demand forecasting