标题： 预测匿名化的电力负荷曲线 显示英文标题

标题： Forecasting Anonymized Electricity Load Profiles

摘要： 在数据隐私不断演变的背景下，电力负荷曲线的匿名化已成为一个关键问题，尤其是在欧洲实施通用数据保护条例（GDPR）之后。这些电力负荷曲线是能源行业的重要数据集，被归类为个人行为数据，需要严格的保护措施。本文探讨了这种分类的影响、数据匿名化的重要性以及使用微聚类数据进行预测的潜力。研究结果强调，在某些条件下（即预测聚合数据时），有效的匿名化技术（如微聚类）不会影响预测模型的性能。在这样的聚合水平上，微聚类数据保持了高水平的实用性，对预测准确性的影响很小。对能源行业而言，这意味着隐私保护的数据实践可以融入智能计量技术应用中，而不会妨碍其有效性。 显示英文摘要

摘要： 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.