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@inproceedings{Chhachhi:2021:10.1109/ISGT49243.2021.9372228,
author = {Chhachhi, S and Teng, F},
doi = {10.1109/ISGT49243.2021.9372228},
pages = {1--5},
publisher = {IEEE},
title = {Market value of differentially-private smart meter data},
url = {http://dx.doi.org/10.1109/ISGT49243.2021.9372228},
year = {2021}
}

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TY  - CPAPER
AB  - 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.
AU  - Chhachhi,S
AU  - Teng,F
DO  - 10.1109/ISGT49243.2021.9372228
EP  - 5
PB  - IEEE
PY  - 2021///
SN  - 2167-9665
SP  - 1
TI  - Market value of differentially-private smart meter data
UR  - http://dx.doi.org/10.1109/ISGT49243.2021.9372228
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000662927400077&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://ieeexplore.ieee.org/document/9372228
UR  - http://hdl.handle.net/10044/1/99448
ER  -

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