Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISGT49243.2021.9372228 | 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) |
Keywords | DocType | ISSN |
data markets,differential privacy,load forecasting,smart grid,smart meters | Conference | 2167-9665 |
ISBN | Citations | PageRank |
978-1-7281-8898-0 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saurab Chhachhi | 1 | 0 | 0.34 |
| Fei Teng | 2 | 10 | 6.45 |