Presentation given at the 1st Cognitive Internet of Things Technologies (COIOTE 2014) October 27, 2014, Rome, Italy The paper is available on the PORTO open access repositor of Politecnico di Torino: http://porto.polito.it/2570936/

1. 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

2. 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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3. 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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4. Can we improve energy efficiency in homes…
•without a metering system?
•with a “coarse grained” metering system?
Yes.
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5. 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

6. 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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7. 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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8. 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”

9. Example
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10. 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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11. 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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12. 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

13. 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
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14. 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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15. 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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16. 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

17. 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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18. Thank you!
Luigi De Russis
luigi.derussis@polito.it