Access to granular demand data is essential for the net zero transition; it allows for accurate profiling and active demand management as our reliance on variable renewable generation increases. However, public release of this data is often impossible due to privacy concerns. Good quality synthetic data can circumnavigate this issue. Despite significant research on generating synthetic smart meter data, there is still insufficient work on creating a consistent evaluation framework. In this paper, we investigate how common frameworks used by other industries leveraging synthetic data, can be applied to synthetic smart meter data, such as fidelity, utility and privacy. We also recommend specific metrics to ensure that defining aspects of smart meter data are preserved and test the extent to which privacy can be protected using differential privacy. We show that standard privacy attack methods like reconstruction or membership inference attacks are inadequate for assessing privacy risks of smart meter datasets. We propose an improved method by injecting training data with implausible outliers, then launching privacy attacks directly on these outliers. The choice of $\epsilon$ (a metric of privacy loss) significantly impacts privacy risk, highlighting the necessity of performing these explicit privacy tests when making trade-offs between fidelity and privacy.

通过研究其他行业采用的常见框架，该论文调查了如何将合成智能电表数据应用于合成数据的一致性评估框架，并建议具体的指标来确保智能电表数据的定义方面得以保留，并测试使用差分隐私保护隐私的程度。结果表明，标准的隐私攻击方法不适用于评估智能电表数据集的隐私风险，论文提出了一种改进方法，并通过注入训练数据中不合理的异常值，直接对这些异常值进行隐私攻击，强调在保证准确性和隐私性之间权衡时，ε（隐私损失的度量）的选择显著影响隐私风险，并提出了明确的隐私测试的必要性。

定义“好”：合成智能电表数据评估框架