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SSN2018 workshopDemo: Integrating Building Information Modelingand Sensor Observations using Semantic WebMads Holten Rasmussen1,2 | Christian Aaskov Frausing1,2 | Christian Anker Hviid1 | Jan Karlshøj1
1: Technical University of Denmark [DTU] | 2: Niras
October 9th 2018
01Baggrund
2 / 28 SSN2018 [email protected]
About me
2011 B.Sc. Architectural engineering, DTU
2013 M.Sc. Architectural Engineering, DTU
20132016
HVAC-engineer, NIRAS (former ALECTIA)2,100 employees - offices in 27 countries
2016 Industrial PhD
”Digital Infrastructure and Building Information Modeling in the design and planning of building services”
01Baggrund
3 / 28 SSN2018 [email protected]
Disposition
01 Problem in scope
02 Linked Building Data
03 The case
04 Implementation
05 Final Words
01Problem in Scope
01Baggrund
5 / 28 SSN2018 [email protected]
Overall information flow
Architect
Client
GeometrySpace function
Indoor Climaterequirements
Control strategy Control strategy
Indoor Climate & Low voltageEnergy engineer engineer
Electrician
01Baggrund
6 / 28 SSN2018 [email protected]
Data exchange of today
Architect
Client
GeometrySpace function
Indoor Climaterequirements
Control strategy Control strategy
Indoor Climate & Low voltageEnergy engineer engineer
Electrician
01Baggrund
7 / 28 SSN2018 [email protected]
Change management
Architect
Client
GeometrySpace function
Indoor Climaterequirements
Control strategy Control strategy
Indoor Climate & Low voltageEnergy engineer engineer
Electrician
01Baggrund
8 / 28 SSN2018 [email protected]
Architect
Client
GeometrySpace function
Indoor Climaterequirements
Control strategy Control strategy
Indoor Climate & Low voltageEnergy engineer engineer
Electrician
Change management
01Baggrund
9 / 28 SSN2018 [email protected]
Future scenario
Building ManagementSystem
Data processingKnowledge base
ActualconditionsFindings
Future buildingsControl strategy tweaks
Client
01Baggrund
Slidetitel
10 / 28 SSN2018 [email protected] / 28 SSN2018 [email protected]
How to integrate the architectural model with the engineer’s demands and the actual systems installed in the future building?
Linked Building Data
02
01Baggrund
13 / 28 SSN2018 [email protected]
OPM
BOT The Building Topology Ontology Products’ ontology
Properties’ ontologyOntology for Property Management
PRODUCT
PROPS
https://w3id.org/bot# https://w3id.org/product#
For describing any zone orelement in its context of thebuilding in which it belongs
For describing properties with relation to buildings
For describing productswith relation to buildings
For describing temporal design properties that arelikely subject to changes
https://w3id.org/opm# https://w3id.org/props#
01Baggrund
14 / 28 SSN2018 [email protected]
BOT Main Classes
bot:Elementbot:Zone
bot:Site bot:Building bot:Storey bot:Space
A spatial 3D division. An instance of bot:Zone can contain other bot:Zone instances, making it
possible to group or subdivide zones.
Zone Subclasses
Constituent of a construction entity with a characteristic technical function,
form or position
01Baggrund
15 / 28 SSN2018 [email protected]
BOTSelected relationships
bot:Elementbot:Zone
arch:spaceB arch:WC1arch:spaceA
bot:adjacentZone bot:containsElement
rdf:type rdf:typerdf:type
ABoxTBox
The case
03
01Baggrund
18 / 28 SSN2018 [email protected]
Architectural modelexported from BIMauthoring tool
inst:site_1 a bot:Site ; bot:hasBuilding inst:building_1 .
inst:building_1 a bot:Building ; bot:hasStorey inst:level_1xS3BCk291UvhgP2dvNMKI .
inst:level_1xS3BCk291UvhgP2dvNMKI a bot:Storey ; bot:hasSpace inst:room_1xS3BCk291UvhgP2dvNtzh .
inst:room_1xS3BCk291UvhgP2dvNtzh a bot:Space ; props:identityDataName "Kontor 01.063" ; props:identityDataNumber "01.063" ; props:dimensionsArea "12.71 m2"^^cdt:area ; props:spaceBoundary "POLYGON((-49111 -5192, (...), -49111 -5192))"^^geo:wktLiteral .
01Baggrund
19 / 28 SSN2018 [email protected]
Architectural modelexported from BIMauthoring tool
Sensor observationsextracted fromspreadsheets
inst:room_01.063-Temp-Sensor-obs0 a sosa:Observation ; sosa:hasFeatureOfInterest inst:room_1xS3BCk291UvhgP2dvNtzh ; sosa:hasResult "20.7 Cel"^^cdt:temperature ; sosa:madeBySensor inst:room_01.063-Temp-Sensor ; sosa:observedProperty inst:room_01.063-Temp ; sosa:resultTime "2017-04-18T05:11:32+01:00"^^xsd:dateTime .
01Baggrund
20 / 28 SSN2018 [email protected]
Sensor observationsextracted fromspreadsheets
Prototype:Tool for equippingduring design
ng-plan
01Baggrund
21 / 28 SSN2018 [email protected]
Data structure
bot:Building
bot:Zone
inst:bldg inst:lvl
rdf:typerdf:type rdf:typebot:hasStorey bot:hasSpace
bot:containsZone bot:containsZone
props:spaceBoundary
bot:hasSimple3DModel
“Office 225”@en
props:identityDataName
bot:containsZone
bot:Space
inst:sp
bot:Storey
“POLYGON(0 0, 0 6500, 6500 4700, 0 4700, 0 0)”^^geo:wktLiteral
ABox
TBox
“v 0 0 0v 0 6500 0v 0 6500 2750n 0 0 1f 1//1 2//1 3//1...”^^fileinfo:obj
01Baggrund
22 / 28 SSN2018 [email protected]
Data structure
sosa:observes
sosa:madeBySensor
sosa:hasResultsosa:resultTime
inst:sensor inst:temp inst:obs
rdf:typerdf:type
rdf:type
sosa:Sensor
sosa:Observation
sosa:ObservableProperty
“22.4 Cel”^^cdt:temperature
“2017-11-11T23:47:44+01:00”^^xsd:dateTime
ABox
TBox
01Baggrund
23 / 28 SSN2018 [email protected]
Data structure
bot:Building
bot:Zone
inst:bldg inst:lvl
rdf:typerdf:type rdf:typebot:hasStorey bot:hasSpace
bot:containsZone bot:containsZone
bot:containsZone
bot:Space
inst:sp
bot:containsElement
sosa:hosts
sosa:observes
sosa:madeBySensor
sosa:hasFeatureOfInterest
inst:sensor inst:temp inst:obs
rdf:typerdf:type
rdf:type
bot:Element
sosa:Sensor
sosa:Observation
sosa:ObservableProperty
bot:Storey
ABox
TBox
bot:containsElement
bot:containsElement
Implementation
04
Final words
05
01Baggrund
27 / 28 SSN2018 [email protected]
Conclusions
• Data model developed organically as project evolves
• Explicit information exchanges through all processes
• DEMO illustrates the ease of accessing the data
01Baggrund
28 / 28 SSN2018 [email protected]
Future work
• Post-processing of observations
• Use Linked Temperature Data Cube to pre-process the hourly, daily, weekly, monthly and annual min/max/avg temperatures explicitly
•Useinrealprojecttoexamineworkflowsandbenefits
• Development of dedicated tools for describing control strategies etc.
•Optimizecontrolofflowsystemsbasedonobservations
• Re-do thermal simulations with actual occupant loads and weather data