March 30, 2015

Hao Zhu

Power & Energy Systems Group

Dept. of Electrical & Computer Engineering

University of Illinois, Urbana-Champaign

Making Sense of Big Data – Part 4

Energy Data Disaggregation

About this module

Prof. Hao Zhu (haozhu@)

Office hours (for ECE 330) every Tuesday 11-12:30 (ECEB 4056)

Week 10: motivation and context, data pre-processing

Weeks 11-12: disaggregation methods

Two TAs: Max Liu (haoliu6@) and Phuc Huynh (pthuynh2@)

TA office hours?

2

Nikola Tesla George Westinghouse

Thomas Edison

James Clerk Maxwell Source: Creative Commons 3

The electric power grid

Wikipedia: “Power engineering…is a subfield of electrical engineering

that deals with the generation, transmission, distribution and utilization

of electric power.”

211,000 miles of transmission

lines ≥230kV

15,600 power plants

830GW load demand

Source: www.theenergylibrary.com 4

“If I Only Had a Brain”

GE 2009 Super Bowl Ad; www.youtube.com

5

Power balance

One fundamental operational principle is to continuously balance supply and

demand to achieve frequency stability

Various generation control and scheduling schemes (from seconds to weeks)

Source: http://www.okiden.co.jp/english/r_and_d/

The Smarter Grid

Source: http://www.imageslides.com/Technology/gallery/11604-Inside-a-power-grid-control-room-(photos)

Electric utilities have been leaders in using technology

Supervisory control and data acquisition (SCADA) systems:

monitor and operate the high-voltage transmission systems

7

Smart distribution systems

Distribution systems traditionally considered to be

very passive, with little real-time data and control

How does the power company learn that you've

lost power? When you call on the phone. – An

article in the National Geographic magazine

Distribution automation has been making steady

advances for many years, a trend that should

accelerate with smart grid funding

S&C IntelliRupter® PulseCloser

Elster REX digital meter

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Smart Meters

An electronic device that records electric energy consumption in intervals of

an hour or less and communicates at least daily back to the utility

Utility-level applications: power outage detection/localization

9 http://blog.opower.com/2014/07/data-algorithm-smart-grid-without-smart-meters/

Consumer-level: smart homes?

Customers can examine time-specific energy use, see how they compare within their

neighborhood, understand how and why energy use varies over time, and ect.

My Energy portal provided by Pacific Gas & Electric (PG&E)

10

Energy Saving!

Disaggregated energy data

Disaggregation allows us to take a whole building (aggregate) energy signal,

and separate it into appliance specific data (i.e., plug or end use data).

11

Why appliance-level feedback?

12

Non-intrusive load modeling

Power engineers (including RLE, MIT) have investigated it since 1990s

Prior approaches: edge detection, real/reactive power signature analysis, and

higher-order harmonics analysis

Success requires high-precision metering, mainly used for motor diagnostics

13 Steady-state power consumption of a computer and a bank of incandescent lights

Recent growth

Number of publications rise in last five years

14 http://blog.oliverparson.co.uk/

Disaggregation options

Smart Meter is the lowest-cost & lowest installation effort sensor for consumers

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Data requirements

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Ultra-high frequency data

A recent approach using electromagnetic interference (EMI) at MHz frequency

developed at Uwashington

Specific sensors add up the costs in prototype systems

17 http://youtu.be/o-SqO8y8XUA

Belkin energy disaggregation competition

A competition ($25k) on Kaggle from Jul 2 to Oct 30, 2013

EMI-based dataset for appliance use detection and classification

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Smart meter hardware capabilities

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Implementation options

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Commercial solutions

Bidgely, (formerly MyEnerSave), CA, USA

LoadIQ, NV, USA

PlotWatt, NC, USA

Verlitics, (formerly Emme), OR, USA

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HOMEBEAT ENERGY MONITOR EI.X Series Monitor

Our focus

Minute-second resolution of power consumption data

Well supported by the existing smart metering infrastructure

Reference Energy Disaggregation Data Set (REDD): contains both household-

level and circuit-level data from 6 US households, over various durations

Learning approaches for non-event based disaggregation

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References

Carrie Armel, K., Gupta, A., Shrimali, G., and Albert, A. Is disaggregation the

holy grail of energy efficiency? The case of electricity. Energy Policy 52,

(2012), 213–234.

Carrie Armel, Energy Disaggregation, Precourt Center, Stanford, 2013

Christoper Laughman, et al. "Power signature analysis." IEEE Power and

Energy Magazine, 1.2 (2003): 56-63.

Steven Shaw, et al. "Nonintrusive load monitoring and diagnostics in power

systems." IEEE Trans. Instrumentation and Measurement, 57.7 (2008): 1445-

1454.

Sidhant Gupta, et al. "ElectriSense: single-point sensing using EMI for

electrical event detection and classification in the home." Proc. 12th ACM Intl

Conf. on Ubiquitous computing, 2010. 23