用匿名化数据预测用电量，隐私与精度可兼得。
Forecasting Anonymized Electricity Load Profiles
- 对用电数据进行微聚合匿名处理，保护隐私。
- 在聚合层面预测时，准确率损失小于5%。
- 适合关注数据隐私的智能电网研究者。
随着欧洲通用数据保护条例（GDPR）的实施，电力负荷数据的匿名化成为关键问题。这类数据被认定为个人行为数据，需严格保护。本文探讨了匿名化的重要性及基于微聚合数据进行预测的潜力。研究表明，在特定条件下（即聚合级别预测），微聚合技术不会显著影响预测模型性能，其数据实用性高，对预测精度影响极小。这一发现表明，隐私保护的数据处理方法可无缝集成至智能电表应用中，不影响能源领域的实际效能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。