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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