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Load Hiding to Preserve Privacy from Smart Meter Measurements Ryan Fraser Advisor: Dr. Kevin Tomsovic Ailin Asadinejad Final Project Presentation July 14, 2016 Knoxville, Tennessee Smart Meters United States Smart Meter Use, 2014 Provide

SLIDE 1

Load Hiding to Preserve Privacy from Smart Meter Measurements

Ryan Fraser Advisor: Dr. Kevin Tomsovic Ailin Asadinejad

Final Project Presentation July 14, 2016 Knoxville, Tennessee

SLIDE 2

Smart Meters Provide More information

More accurate forecasting Fault detection Increased information to consumers Increased stability for renewables and energy storage

United States Smart Meter Use, 2014

SLIDE 3

Non-Invasive Load Monitoring (NILM) Capable of Identifying Specific Appliances Loss of privacy Map activities and behaviors of homeowners

SLIDE 4

Previous Research Battery-based Load Monitoring

5 10 15 20

Time

hrs 200 400 600 800 1000 1200 1400 1600

Power Demand (W)

Home Power Use

5 10 15 20

Time

hrs

1000
500

500

Power Demand (W)

Battery

5 10 15 20

Time

hrs 579 579.2 579.4 579.6 579.8 580 580.2 580.4 580.6 580.8 581

Power Demand (W)

Power Read by Meter

SLIDE 5

Project Goal

Design a system to improve individual household privacy Maintain the benefits provided by smart meters

SLIDE 6

Information Theory & Entropy Entropy is the measure of unpredictability of information

= − ∑ log (

)

:

= ⁄

SLIDE 7

Load Imitating Add false loads in order to increase system entropy

Model of Television Power Demand Model of Lighting Power Demand

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Data

SLIDE 9

Analyzed Loads

SLIDE 10

Formula = 1 2 + − ()

when > 0

battery discharges < 0 battery charges Probability of Load Occurring = + ()

SLIDE 11

Results

8 8.5 9 9.5 10 10.5 11 11.5 hrs

200 400 600 800 1000

Imitation Original

SLIDE 12

Results

SLIDE 13

Possible Further Work Design to work with non-time based loads Optimize with a battery Test with more realistic data Test against NILM

SLIDE 14

Acknowledgements

This work was supported primarily by the ERC Program of the National Science Foundation and DOE under NSF Award Number EEC-1041877. Other US government and industrial sponsors of CURENT research are also gratefully acknowledged. 14

SLIDE 15

Load Hiding to Preserve Privacy from Smart Meter Measurements

Ryan Fraser Advisor: Dr. Kevin Tomsovic Ailin Asadinejad

Smart Meters Provide More information

More accurate forecasting Fault detection Increased information to consumers Increased stability for renewables and energy storage

Non-Invasive Load Monitoring (NILM) Capable of Identifying Specific Appliances Loss of privacy Map activities and behaviors of homeowners

Previous Research Battery-based Load Monitoring

Project Goal

Design a system to improve individual household privacy Maintain the benefits provided by smart meters

Information Theory & Entropy Entropy is the measure of unpredictability of information

= − ∑ log (

:

= ⁄

Load Imitating Add false loads in order to increase system entropy

Data

Analyzed Loads

Formula = 1 2 + − ()

battery discharges < 0 battery charges Probability of Load Occurring = + ()

Results

Results

Possible Further Work Design to work with non-time based loads Optimize with a battery Test with more realistic data Test against NILM

Acknowledgements

This work was supported primarily by the ERC Program of the National Science Foundation and DOE under NSF Award Number EEC-1041877. Other US government and industrial sponsors of CURENT research are also gratefully acknowledged. 14

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