Load hiding of household's power demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.

[1]  Stephen B. Wicker,et al.  Inferring Personal Information from Demand-Response Systems , 2010, IEEE Security & Privacy.

[2]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[3]  H. Scott Matthews,et al.  One size does not fit all: Averaged data on household electricity is inadequate for residential energy policy and decisions , 2013 .

[4]  Georgios Kalogridis,et al.  Smart Grid Privacy via Anonymization of Smart Metering Data , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[5]  Georgios Kalogridis,et al.  Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[6]  Ulrich Greveler,et al.  Multimedia Content Identification Through Smart Meter Power Usage Profiles , 2012 .

[7]  Peng Liu,et al.  Secure and privacy-preserving information aggregation for smart grids , 2011, Int. J. Secur. Networks.

[8]  Yuan Qi,et al.  Minimizing private data disclosures in the smart grid , 2012, CCS '12.

[9]  Dominik Egarter,et al.  Appliance State Estimation based on Particle Filtering , 2013, BuildSys@SenSys.

[10]  Tuan Anh Nguyen,et al.  Energy intelligent buildings based on user activity: A survey , 2013 .

[11]  Abhay Gupta,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .

[12]  Florian Skopik,et al.  Security Is Not Enough! On Privacy Challenges in Smart Grids , 2012 .

[13]  Patrick D. McDaniel,et al.  Protecting consumer privacy from electric load monitoring , 2011, CCS '11.

[14]  Andrea Monacchi,et al.  GREEND: An energy consumption dataset of households in Italy and Austria , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[15]  Andrea Monacchi,et al.  Strategies for domestic energy conservation in Carinthia and Friuli-Venezia Giulia , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[16]  Dominik Egarter,et al.  PALDi: Online Load Disaggregation via Particle Filtering , 2015, IEEE Transactions on Instrumentation and Measurement.

[17]  Michael Zeifman,et al.  Nonintrusive appliance load monitoring: Review and outlook , 2011, IEEE Transactions on Consumer Electronics.