Recent Advances in Algorithms for Energy Disaggregation · Recent Advances in Algorithms for Energy...

Recent Advances in Algorithms for Energy Disaggregation

J. Zico Kolter MIT Computer Science and Artificial Intelligence Laboratory

BECC Conference, 2011

In collaboration with: Tommi Jaakkola, Matthew J. Johnson, Andrew Y. Ng, Sidhartha Batra

J. Zico Kolter | Recent Advances in Algorithms for Energy Disaggregation

The Goal

• Determine breakdown of power given whole-home consumption (e.g. from smart meter)

Total Power


The Goal

• Determine breakdown of power given whole-home consumption (e.g. from smart meter)

Total Power Disaggregated Power


“Event-Based” Detection



Dryer? Fridge? Microwave? Noise? …



Dryer? Fridge? Microwave? Noise? …

• The standard paradigm for energy disaggregation (e.g, Hart, 1992; Leeb, 1993; Laughman et al., 2003; Patel et al., 2006, Bergés et al., 2010; Gupta et al., 2010)

• Problems: doesn’t provide actual energy breakdown, difficultly handling long-term devices, can make physically impossible predictions (e.g., device comes on twice without turning off)


“Power Packing” Detection



Coffee Machine

Dryer

Computer



• Helped by nature of AC Power

Curr

ent Time =

Coffee Machine

Dryer

Computer



• A relatively recent approach (Suzuki et al., 2008; Liang et al., 2010; Inagaki et al., 2011)

• Provides true energy breakdown, can handle “always on” devices

• Problems: combinatorial (hard) optimization problem, extremely sensitive to noise/transients and devices that are too similar

Our work: Algorithms for “Joint” Energy Disaggregation

[Kolter et al., 2010; Kolter and Jaakkola, 2011]


Interconnected Prediction Tasks


Modeling Interconnected Tasks

• Powerful methods for handling such tasks: probabilistic graphical models

• Exactly solving resulting optimization problem still intractable

Factorial Hidden Markov Model [Ghahramani and Jordan, 1997]


Modeling Interconnected Tasks

• Powerful methods for handling such tasks: probabilistic graphical models

• Exactly solving resulting optimization problem still intractable

Factorial Hidden Markov Model [Ghahramani and Jordan, 1997]

• Our method presents an approach for approximately solving such optimization problems; based on methods from convex optimization, probabilistic inference

• Incorporates both the “event-based” features and the “power packing” features, classifies all energy jointly

• Tractable for large numbers of devices, many states for each device, and long time periods


Performance Total Power


Performance True Disaggregation


Performance Our Method


Performance


Performance Over Two Weeks

Device (Circuit) Our Method Previous Approach

Microwave 97.5% 94.8%

Bath GFI 15.0% 80%

Bath GFI 95.1% 0%

Bath GFI 94.7% 37.4%

Electronics 41.6% 39.4%

Kitchen Outlets 37.5% 9.7%

Furnace 91.7% 18.5%

Kitchen Outlets 45.2% 12.5%

Washer / Dryer 98.8% 89.3%

Total 87.2% 44.7%

Publicly Available Data and the Behavior / Algorithms

Interplay


REDD: A Publicly Available Dataset

• Reference Energy Disaggregation Data Set • 16.6 kHz whole-home current

and voltage • 1 Hz power consumption for

each circuit (Powerhouse Dynamics eMonitor)

• 1 Hz power consumption on select outlets (Enmetric PowerPort)

Initial release (6 homes, ~2 weeks) available at: http://redd.csail.mit.edu

[Kolter and Johnson, 2011]

http://redd.csail.mit.edu/


REDD: A Publicly Available Dataset

• Reference Energy Disaggregation Data Set • 16.6 kHz whole-home current

and voltage • 1 Hz power consumption for

each circuit (Powerhouse Dynamics eMonitor)

• 1 Hz power consumption on select outlets (Enmetric PowerPort)

Initial release (6 homes, ~2 weeks) available at: http://redd.csail.mit.edu

• >12 GB of data, per home, per day • Compress using greedy approximation

to total variation regularization

[Kolter and Johnson, 2011]

http://redd.csail.mit.edu/


Behavior and Algorithms

Behavioral Studies

Algorithmic Advances

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