PowerOnt AN ONTOLOGY-BASED APPROACH FOR POWER CONSUMPTION ESTIMATION IN SMART HOMES

Dario Bonino, Fulvio Corno, and Luigi De Russis

Politecnico di Torino, e-Lite research group

http://elite.polito.it

Motivations

• Some data

– electricity accounts for 70% of total energy consumption in homes

– around 30% of the total electric energy consumption is

allocated to the residential sector

– both in the EU and in the U.S.

• Smart homes can help in reducing global home consumptions

– by suggesting more efficient behavior

– by postponing the activation of energy greedy appliances

– etc.

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What we need?

• Home automation system

– as a prerequisite for the creation of a smart home

– wireless, wired, old, new…

• Metering system

– key factor for “energy positive” innovations in homes

– must be “fine grained”

– integrated with the home automation system

– expensive, typically

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Can we improve energy efficiency in homes…

• without a metering system?

• with a “coarse grained” metering system?

Yes.

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Can we improve energy efficiency in homes…

• without a metering system?

• with a “coarse grained” metering system?

Yes.

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We can add explicit, machine understandable

information, in form of appliance-level power

consumption data

Trade off

• What we gain – no installation of new hardware (i.e., meters)

– no money to spend

• What we loose – precision in data

• In some cases, installation of new hardware is not possible – so “approximate” data is better than no data

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Introducing… PowerOnt

• An ontology model (OWL2)

• Lightweight and minimal

• Designed to model nominal, typical and real

power consumption of each device in a home

• Enable power consumption estimations by knowing device activations, only

• Able to scale from no metering system to a fine

grained one

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PowerOnt

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Minimal approach • modeling primitives are reduced to those strictly

needed to support power consumption modeling • relations to described devices/appliances are left

“open”

Example

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PowerOnt sample integration

• “Open” relations were linked with DogOnt concepts

– DogOnt is a OWL2 ontology for modelling Smart

Environments (http://elite.polito.it/ontologies/dogont)

• Integration means – specialize the poweront:consumptionOf range to dogont:Controllable

– specialize the poweront:whenIn range to dogont:StateValue

• Result available at

– http://elite.polito.it/ontologies/poweront.owl

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Example application

• Bathroom with a lamp on the mirror, a ceiling

lamp and a (metered) shutter

• Goal: suggest to home inhabitants what is the

least power consuming device to illuminate the

bathroom

• We exploit PowerOnt integrated with DogOnt

to get this information

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Example application: SPARQL

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SELECT ?device

WHERE

{

?device a dogont:Controllable.

?device dogont:isIn

<http://elite.polito.it/ontologies/samples/sampleHome.owl#Bathroom>.

?consumption a poweront:ElectricPowerConsumption.

?device dogont:hasState ?state.

?state dogont:hasStateValue ?stateValue.

?consumption poweront:consumptionOf ?device.

?consumption poweront:whenIn ?stateValue.

OPTIONAL { ?consumption poweront:actualValue ?consumptionValue }.

OPTIONAL { ?consumption poweront:nominalValue ?consumptionValue }.

OPTIONAL { ?consumption poweront:typicalValue ?consumptionValue }.

}

ORDER BY ASC(?consumptionValue)

LIMIT 1

Example application: SPARQL

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SELECT ?device

WHERE

{

?device a dogont:Controllable.

?device dogont:isIn

<http://elite.polito.it/ontologies/samples/sampleHome.owl#Bathroom>.

?consumption a poweront:ElectricPowerConsumption.

?device dogont:hasState ?state.

?state dogont:hasStateValue ?stateValue.

?consumption poweront:consumptionOf ?device.

?consumption poweront:whenIn ?stateValue.

OPTIONAL { ?consumption poweront:actualValue ?consumptionValue }.

OPTIONAL { ?consumption poweront:nominalValue ?consumptionValue }.

OPTIONAL { ?consumption poweront:typicalValue ?consumptionValue }.

}

ORDER BY ASC(?consumptionValue)

LIMIT 1

PowerOnt

Example application

• By knowing that the shutter is the least consuming device, a software can check other environmental properties (e.g., outside lighting) and decide to move up the shutter, instead of turn on a lamp

• Moreover, if only one meter is available for measuring the three device consumptions, a software component can exploit PowerOnt to “disaggregate” their power consumptions – by using nominal, typical, or real values to split the

overall measurement

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What about data precision?

• We loose precision by modeling a device state

for its typical, nominal and measured power consumptions

– typical values give the less precise information

– measured values give the most precise information

• In general, the precision of the consumption

estimation increase with the number of “real”

meters

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What about data precision?

• Desk Lamp, turned on

• Microwave oven, turned on

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Typical Nominal Measured

40 W 18 W 20.5 W

Typical Nominal Measured

1510 W 900 W 1300 W

Conclusions

• PowerOnt is a lightweight ontology for

modeling power consumptions in smart homes

• It needs to be integrated with another ontology

representing smart home devices

• It enables “energy saving” scenarios even with no metering system

• A software component of a smart home

middleware that uses PowerOnt is currently in the final stages of development

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Thank you!

Luigi De Russis

luigi.derussis@polito.it