Paper Info
Title
Seer Grid: Privacy and Utility Implications of Two-Level Load Prediction in Smart Grids.
Abstract
We propose “Seer Grid”, a novel two-level energy consumption prediction framework for smart grids, aimed to decrease the trade-off between privacy requirements (of the customer) and data utility requirements (of the energy company (EC)). The first-level prediction at the household level is performed by each smart meter (SM), and the predicted energy consumption pattern (instead of the actual energy usage data) is reported to a cluster head (CH). Then, a second-level prediction at the neighborhood level is done by the CH which predicts the energy spikes in the neighborhood or cluster and shares it with the EC. Our two-level prediction mechanism is designed such that it preserves the correlation between the predicted and actual energy consumption patterns at the cluster level and removes this correlation in the predicted data communicated by each SM to the CH. This maintains the usefulness of the cluster-level energy consumption data communicated to the EC, while preserving the privacy of the household-level energy consumption data against the CH (and thus the EC). Our evaluation results show that Seer Grid is successful in hiding private consumption patterns at the household-level while still being able to accurately predict energy consumption at the neighborhood-level.
Year
DOI
Venue
2017
10.1109/TPDS.2016.2564399
IEEE Trans. Parallel Distrib. Syst.
Keywords
Field
DocType
Energy consumption,Data privacy,Batteries,Smart grids,Privacy,Smart meters,Correlation
Smart grid,Computer science,Smart meter,Information privacy,Usage data,Energy consumption,Grid,Distributed computing
Journal
Volume
Issue
ISSN
28
2
1045-9219
Citations 
PageRank 
References 
1
0.35
29
Authors
5
Name
Order
Citations
PageRank
Arash Boustani131.05
Ananda Maiti2439.63
Sina Yousefian Jazi310.35
Murtuza Jadliwala426625.26
Vinod Namboodiri534727.79