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
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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
8
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
15
Data requirements
16
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
18
Smart meter hardware capabilities
19
Implementation options
20
Commercial solutions
Bidgely, (formerly MyEnerSave), CA, USA
LoadIQ, NV, USA
PlotWatt, NC, USA
Verlitics, (formerly Emme), OR, USA
21
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
22
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
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