Market Value of Differentially-Private Smart Meter Data
Market Value of Differentially-Private Smart Meter Data
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DOI：
10.1109/isgt49243.2021.9372228
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Saurab Chhachhi;F. Teng
中科院分区：
文献类型：
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作者：
Saurab Chhachhi;F. Teng
本文提出了一个框架，以调查国内消费者和负荷服务实体之间共享隐私保护的智能电表数据的价值。该框架包括一个折扣差分隐私模型，以确保个人无法从聚合数据中识别，基于人工神经网络的短期负荷预测，以量化数据可用性和隐私保护对预测误差的影响，以及一天前的最佳采购问题和平衡市场，以评估隐私效用权衡的市场价值。该框架表明，当消费者群体的负荷分布不同于系统平均值时，这是使用Kullback-Leibler分歧量化的，在共享智能电表数据的同时保留个人消费者隐私具有重要价值。
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.