Exploiting Home Automation Protocols for Load …lass.cs.umass.edu/slides/2011/buildsys11-insteon.pdfDepartment of Computer Science Exploiting Home Automation Protocols for Load Monitoring

Department of Computer Science

Exploiting Home Automation Protocols for Load Monitoring in

Smart Buildings

David Irwin, Anthony Wu†, Sean Barker,Aditya Mishra, Prashant Shenoy, Jeannie Albrecht‡

University of Massachusetts AmherstAmherst College†Williams College‡

University of Massachusetts Amherst - Department of Computer Science

Why Smart Buildings?

~70% of grid power usage Smart buildings for

grid efficiency

Economic benefits Environmental benefits

2


Demand-Side Energy Management

Managing energy usage• Shifting loads• Reducing loads

DSEM components:• Continuous energy monitoring• Load control

3

Peak Usage Off-Peak Usage

ShiftableLoad

onoff

(a) ACme consists of five pri-

mary components: current-to-

voltage conversion, energy me-

tering, AC/DC power supply,

microcontroller and radio, and

solid state relay.

(b) ACme-A uses shunt resistor as I-V

conversion, direct rectification as AC/DC

power supply, and ADE7753 as the energy

metering IC.

(c) ACme-B uses an in-line Hall Effect

sensor as I-V conversion, a step-down

transformer followed by a bridge rectifier

as the AC/DC power supply, and performs

energy calculation in software using the

microcontroller.

Figure 2: ACme architecture and simplified schematics.

of the inductive coupling. These sensors have the ob-

vious benefit of non-intrusive measurement. However,

they also have two flaws: (1) the wires in the AC line

need to be physically separated so that the sensor can

attach to the phase wire, and (2) the meter itself must

be powered by a separate power supply, which means ei-

ther batteries or a separate AC-DC converter is needed.

The clamp-on option is attractive for branch level me-

tering, but not ideal for receptacle level.

3.1.3 Hall Effect Sensor

A third method of converting current to voltage is the

Hall Effect sensor. These devices use the Hall Effect [4]

to measure current and can be either clamp-on (non-

contacting) or in-line. The clamp-on form factor is not

considered here for reasons above.

In-line Hall Effect sensors intercept the AC current

and couple it with an internally calibrated Hall Effect

element. This approach is compact and precise. More

importantly, the high voltage AC input is electrically

isolated from the low voltage output inside the in-line

Hall Effect sensor, providing an electric isolation of kilo-

volts. This makes it possible to use an efficient step-

down transformer as the power supply. The step-down

transformer also establishes a ground at a safe, low volt-

age. ACme-B is designed using this approach, and is

shown in Figure 2(c).

3.2 Energy MeteringEnergy metering is the process of calculating the power

and energy from the current and voltage. This process

can be done in either software or hardware. The two

methods have different tradeoffs and are appropriate for

different applications.

In the software method, a single wire connects the

output from the I-V conversion to the microcontroller’s

ADC. This pushes measurement into the microcontroller,

which must sample the signals, multiply, and accumu-

late in software. While this choice avoids the need for

a dedicated meter IC, the microcontroller is kept busy

performing the sampling, and the results are less pre-

cise due to lower sampling rates. In the case of ACme-

B, we cannot connect the AC wires to the microcon-

troller directly to obtain the voltage because a trans-

former is used as the power supply. We assume a con-

stant RMS voltage in converting current to power. This

is acceptable for applications which monitor only appar-

ent power.

There are many commercial ICs that perform energy

measurement in hardware. For example, Microchip’s

MCP3905 supports real power measurement using two

ADC channels, one for current and one for voltage. The

output is a pulse whose frequency is proportional to the

power. However, it does not support energy accumu-

lation, requiring another chip or the microprocessor for

computing energy. The analog pulse output requires

either constant sampling using ADC or triggering an

interrupt on every pulse, further burdening the micro-

controller. Analog Devices’s ADE7753 provides real,

reactive, and apparent power calculations. It internally

integrates power to produce energy, provides extensive

filtering, and includes a temperature sensor. ADE7753

stores power and energy measurements in registers, and

communicates with the microcontroller via the SPI bus.


Energy Monitoring Systems

Primary goals• Inexpensive• Non-invasive

Examples• ViridiScope [Ubicomp 09]• ACme [SenSys 09]• Single-point sensing• ElectriSense [Ubicomp 10]• Flick of a Switch [Ubicomp 07]

• Many others

Monitoring ≠ Control

4


Exerting Control on Electrical Loads

Home automation (HA) products

Inexpensive and mature• X10 (1975)• Insteon (2001)• Z-Wave (2005)

Already deployed in smart grid trials

5

www.insteonsmartgrid.com

http://www.insteonsmartgrid.com

http://www.insteonsmartgrid.com


Combining Monitoring and Control

Load control requires adding HA-like hardware to devices

Augment HA with monitoring

Challenges:(1) Very low bandwidth and primitive HA protocols

(2) Coarse-grained events rather than fine-grained data streams

(3) Mapping events to power data

6

on / offevents

powerdata


Our System: AutoMeter

AutoMeter: our HA system for building-wide monitoring

Low-cost, off-the-shelf components

Wall switches• on/off/dim event notifications

Power meters• queries for outlet-level data

Prototype deployment in a home

7


AutoMeter Architecture

8

Panel AutoMeter Controller

Building/CircuitPower

Switch Events

LoadDisaggregation

PlugPower


System Components

Using the Insteon HA protocol

Why Insteon?• Low cost (~$40 per device)• More reliable than X10• Non-proprietary• Not solely reliant on wireless

Complications• Reverse engineering meter protocol• No notifications from meters• Usable bandwidth <180 bps

9

level changed?

...


Insteon Protocol Overview

Increasing message reliability:• Message propagation, ACKs, retransmissions

Bandwidth limits:• Theoretical: 2880 bps, practical: <180 bps

10

PLMSwitch

1

Plug 2

Plug 1

Plug 3

Switch 2

hops = 3

hops

= 3

hops = 2

hops = 3

hops = 2

hops = 1

hops = 1

hops = 2

hops

= 2

ControllerUSB

PowerLine

"Query Plug 3"


Current AutoMeter Deployment

3 bedroom, 2 bath house, 34 wall switches• 20 Insteon SwitchLinc relays• 10 Insteon SwitchLinc dimmers• 30 Insteon iMeter Solos• TED 5000 for aggregate readings• GuruPlug control server connected to

Insteon PowerLine Modem (PLM)

Entire equipment budget: $3025

96.7% of total TED energy use accounted for in a two-week period

11


Issues Encountered

1. Low bandwidth and message losses• Trading off query rate and reliability

2. Learning switch power usage• Proactive and reactive strategies

3. Tagging aggregate power variations• Remapping power changes back to events

12


Problem 1: Low Data Rates

<180 bps over power line

No collision avoidance• Serial meter queries• Asynchronous switch events

Approach: insert delay between subsequent queries

Delays for single meter multiplied by # of meters!

13

‘query meter’

‘sw

itch

off’

X


Meter Query Losses

Approximate query duration: 1.0333 sec Reliability vs. global query rate Much lower reliability with lower interarrival times

14

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10

% Q

uerie

s R

ecei

ved

Interarrival Time (sec)

Home DeploymentIsolation

Model (no retransmissions)

1.0333s


Wall Switch Event Losses

Cannot control when event messages occur Increase interarrival time to reduce collisions Round-robin queries every 10s (5 min / device)• <5% switch loss probability

15

0 10 20 30 40 50 60 70 80 90

100

0 1 2 3 4 5 6 7 8 9 10

% E

vent

s Lo

st

Interarrival Time (sec)

Home DeploymentModel (no retransmissions)


Smart Polling

Idea: How much energy could we miss between queries to a device?

Cap amount of unaccounted energy Per-device query rate• power usage and typical duration

16

vs.

on or off? on or off?

slow queries fast queries


Problem 2: Learning Switch Power

Switches only report on/off/dim

Goal: learn switch power

Use aggregate TED data

Simple proactive approach• Programmatically disable all loads• Turn device on, record delta• Repeat for each device• 93% accurate, but requires cycling

17

‘power: 100W’

‘switch on’

‘switch usage: 100W’


Reactive Approach: Learning on-the-fly

Learning power values on-the-fly

Problems encountered• Delayed data points• Simultaneous events• Bad readings

Record deltas around events• ‘Bin’ them based on delta size• Avg most common bin (e.g., 55-65W

deltas) as energy value

Intuition: over many events, bins will reveal true value

18

‘last power: 4

32W,

new power: 493W’

‘switch on’

‘delta: 61W,record in 55-65W bin’


Reactive Approach: Binning

Wide range of energy deltas around events Bins usually identify true delta• But...need enough data points• And highly correlated events are bad

19

0

5

10

15

20

25

30

35

40

15-25W

25-35W

35-45W

45-55W

55-65W

65-75W

75-85W

85-95W

95-105W

105-115W

115-125W

125-135W

135-145W

145-155W

155-165W

165-175W

175-185W

Num

ber E

vent

s

Watt Bins

guestbath:overheadlightguestbath:sinklight

masterbath:sinklight


Problem 3: Tagging Power Variations

Objective: tag aggregate data with specific events Problem: errors in aggregate data• Reading errors (2% TED error)• Timing errors (missed readings, 5-minute frequencies)• Rampup errors (TED readings change gradually)

Many events missed even with high deltas

20

0

20

40

60

80

100

0 100 200 300 400 500 600 700 800 900

Per

cent

age

Threshold (W)

Individual Events:Building Events

Figure 6. We use AutoMeter’s switch, plug, and circuitmeters to tag power variations in building-wide data.

over which we detect a state change and tag the point in thebuilding-wide data that matches the state change. However,coarse plug data, in addition to power and timing errors, alsocomplicates tagging events. To quantify how well we areable to tag data using the straightforward approach, Figure 6shows the number of power events from switch, plug, andcircuit meters we are able to tag as a percentage of the totalnumber of events in the building trace, for different powerthresholds. For each threshold value on the x-axis, the y-axisshows the percentage of tagged power events ≥ x in the in-dividual switch, plug, and circuit meters, as compared to thebuilding-wide meter. The figure demonstrates that our plug,switch, and circuit meters have nearly the same events as thebuilding-wide meter as the threshold approaches 900W. For30W-900W thresholds, the number of events in the individ-ual meters is 40-60% of the events in the building-wide data.

Some of the missing events are due to power and tim-ing errors in the building trace, while others are due to the5-minute granularity of our plug meter data. For instance,TED’s stated error is 2% of the total load; if the load is 3kWor greater, power variations of 60W or greater may be theresult of meter inaccuracy. To mitigate errors, we are up-grading to a building-wide meter that timestamps readingsat the point of measurement, uses a reliable transport proto-col to send them to our server, and supports additional, andmore appropriately-sized/accurate, circuit CTs to aid in dis-aggregation. While smart polling should improve the gran-ularity of plug meter data, we are also investigating NILMtechniques using individual circuits in conjunction with ourcoarse plug meter data, similar to those in [15].5 Related Work

There exist a range of systems for monitoring the powerconsumption of electrical loads. These systems present vari-ous tradeoffs in accuracy, monetary cost, installation time,and calibration overhead. Early work on NILM recog-nized the difficulty and cost of instrumenting every individ-ual building load [9]. Thus, NILM focuses on algorithmsfor disaggregating building-wide power to extract individ-ual loads. While useful, NILM also presents challenges, in-cluding collecting accurate load power signatures and distin-guishing loads with similar signatures.

To address NILM’s challenges, a recent approach aug-ments building power meters with heterogeneous sensors,as well as strategically-placed circuit and plug meters [11,12, 15]. The additional data aids in distinguishing eventsin building-wide power data. Another approach is single-

point sensing [8], which monitors AC power to detect pre-cise power signatures at high frequencies, and associate themwith events from specific loads. Our work shows that asso-ciating these events with power data from a building-widemeter also presents challenges. While existing approachesaid in disaggregation, they do not address load control. Un-like load monitoring, control requires integrating additionalcommunication and switch hardware with devices. Since wetarget AutoMeter for smart buildings with HA-driven loadcontrol, it complements dedicated monitoring systems.

An advantage of AutoMeter for researchers is its use ofwidely available commercial out-of-the-box hardware andopen-source software, rather than custom-built research pro-totypes. One goal of AutoMeter is to support higher-levelsmart grid research, e.g., developing load scheduling algo-rithms, improving NILM via machine learning, strengthen-ing smart meter privacy, etc. At $40 each, purchasing 100sof Insteon devices is within the bounds of a modest researchbudget—our deployment, including 30 switches, 30 plugmeters, a GuruPlug, and a TED meter, cost $3025.6 Conclusion and Future Work

In this paper, we demonstrate Insteon’s limitations forload monitoring, and evaluate straightforward load disag-gregation techniques using data from an operational deploy-ment. To further increase AutoMeter’s scalability and accu-racy, as part of future work, we are experimenting with smartpolling to collect more accurate plug meter data, as well asimproved disaggregation techniques. We are also extendingour approach to buildings larger than single-family homes.Large buildings are more challenging, since they have manymore loads (resulting in lower query rates) and longer pow-erlines (resulting in higher loss rates). While originally tar-geted for residential homes, recent work suggests that pow-erline communication is applicable to larger buildings [16].7 References[1] Energy Use for Lighting. http://www.dmme.virginia.gov/DE/

ConsumerInfo/HandbookLighting.pdf.[2] HomePlug Powerline Alliance. http://www.homeplug.org/home/.[3] Insteon for the Smart Grid. http://www.insteonsmartgrid.com.[4] Plogg Wireless Energy Management. http://www.plogginternational.com.[5] Tweet-a-Watt. http://www.ladyada.net/make/tweetawatt/.[6] Insteon: The Details. www.insteon.net/pdf/insteondetails.pdf, 2005.[7] U.S. Department of Energy. Building Energy Data Book. http://

buildingsdatabook.eere.energy.gov/, 2010.[8] S. Gupta, M. Reynolds, and S. Patel. Electrisense: Single-Point Sensing Using

EMI for Electrical Event Detection and Classification in the Home. In UbiComp,2010.

[9] G. Hart. Nonintrusive appliance load monitoring. IEEE, 80(12), December 1992.[10] X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and Implementa-

tion of a High-Fidelity AC Metering Network. In IPSN, 2009.[11] X. Jiang, M. V. Ly, J. Taneja, P. Dutta, and D. Culler. Experiences with a High-

Fidelity Wireless Building Energy Auditing Network. In SenSys, 2009.[12] Y. Kim, T. Schmid, Z. Charbiwala, and M. Srivastava. Viridiscope: Design and

Implementation of a Fine Grained Power Monitoring System for Homes. InUbiComp, 2009.

[13] S. Lanzisera. The “Other” Energy in Buildings: Wireless Power Metering ofPlug-in Devices. Environment Energy Technologies Division Seminar, LawrenceBerkeley National Labs, June 17 2011.

[14] J. Lu, T. Sookoor, V. Srinivasan, G. Ge, B. Holben, J. Stankovic, E. Field, andK. Whitehouse. The Smart Thermostat: Using Occupancy Sensors to Save En-ergy in Homes. In SenSys, 2010.

[15] A. Marchiori and Q. Han. Using Circuit-Level Power Measurements in House-hold Energy Management Systems. In BuildSys, 2009.

[16] P. Pannuto and P. Dutta. Exploring Powerline Networking for the Smart Building.In IP+SN, 2011.

[17] J. Taneja, D. Culler, and P. Dutta. Towards Cooperative Grids: Sensor/ActuatorNetworks for Renewables Integration. In SmartGridComm, 2010.


Conclusions

HA protocols show promise for providing monitoring capabilities• Smart polling• Accurate building data• Other types of data – circuits, topologies, ...

Issues encountered• Switch power: learn proactively or reactively over time• Outlet power: cope with limitations with intelligent polling

Time and cost not a significant barrier for complete HA instrumentation

21

Department of Computer Science

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Exploiting Home Automation Protocols for Load …lass.cs.umass.edu/slides/2011/buildsys11-insteon.pdfDepartment of Computer Science Exploiting Home Automation Protocols for Load Monitoring