This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load profile of a consumer group differs from the system average, which is quantified using the Kullback-Leibler divergence, there is significant value in sharing smart meter data while retaining individual consumer privacy.

翻译：本文件提出一个框架,以调查国内消费者和负载服务实体之间共享受隐私保护的智能计量数据的价值;框架包括一个确保个人无法从汇总数据中识别的折扣差价隐私模型;基于ANN的短期负载预测,以量化数据可用性和隐私保护对预报错误的影响;以及日间最佳采购问题;平衡市场,以评估私利-公用事业交易的市场价值;框架表明,当一个消费者群体的负载状况与系统平均数不同时,如果使用Kullback-Lebeller差异进行量化,共享智能计量数据同时保留个人消费者隐私有重大价值。