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/
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
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PowerOnt: an ontology-based approach for power consumption estimation in Smart Homes
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
â˘Some data
âelectricity accounts for 70% of total energy consumption in homes
âaround 30% of the total electric energy consumption is allocated to the residential sector
âboth in the EU and in the U.S.
â˘Smart homes can help in reducing global home consumptions
âby suggesting more efficient behavior
âby postponing the activation of energy greedy appliances
âetc.
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3. What we need?
â˘Home automation system
âas a prerequisite for the creation of a smart home
âwireless, wired, old, newâŚ
â˘Metering system
âkey factor for âenergy positiveâ innovations in homes
âmust be âfine grainedâ
âintegrated with the home automation system
âexpensive, typically
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4. Can we improve energy efficiency in homesâŚ
â˘without a metering system?
â˘with a âcoarse grainedâ metering system?
Yes.
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5. Can we improve energy efficiency in homesâŚ
â˘without a metering system?
â˘with a âcoarse grainedâ 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
â˘What we gain
âno installation of new hardware (i.e., meters)
âno money to spend
â˘What we loose
âprecision in data
â˘In some cases, installation of new hardware is not possible
âso âapproximateâ data is better than no data
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7. Introducing⌠PowerOnt
â˘An ontology model (OWL2)
â˘Lightweight and minimal
â˘Designed to model nominal, typical and real power consumption of each device in a home
â˘Enable power consumption estimations by knowing device activations, only
â˘Able to scale from no metering system to a fine grained one
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8. PowerOnt
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Minimal approach
â˘modeling primitives are reduced to those strictly needed to support power consumption modeling
â˘relations to described devices/appliances are left âopenâ
10. PowerOnt sample integration
â˘âOpenâ relations were linked with DogOnt concepts
âDogOnt is a OWL2 ontology for modelling Smart Environments (http://elite.polito.it/ontologies/dogont)
â˘Integration means
âspecialize the poweront:consumptionOf range to dogont:Controllable
âspecialize the poweront:whenIn range to dogont:StateValue
â˘Result available at
âhttp://elite.polito.it/ontologies/poweront.owl
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11. Example application
â˘Bathroom with a lamp on the mirror, a ceiling lamp and a (metered) shutter
â˘Goal: suggest to home inhabitants what is the least power consuming device to illuminate the bathroom
â˘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
â˘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
â˘Moreover, if only one meter is available for measuring the three device consumptions, a software component can exploit PowerOnt to âdisaggregateâ their power consumptions
âby using nominal, typical, or real values to split the overall measurement
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15. What about data precision?
â˘We loose precision by modeling a device state for its typical, nominal and measured power consumptions
âtypical values give the less precise information
âmeasured values give the most precise information
â˘In general, the precision of the consumption estimation increase with the number of ârealâ meters
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16. What about data precision?
â˘Desk Lamp, turned on
â˘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
â˘PowerOnt is a lightweight ontology for modeling power consumptions in smart homes
â˘It needs to be integrated with another ontology representing smart home devices
â˘It enables âenergy savingâ scenarios even with no metering system
â˘A software component of a smart home middleware that uses PowerOnt is currently in the final stages of development
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