Demo: Integrating Building Information Modeling and Sensor … · 2018-10-10 · 01 Baggrund 2 / 28...

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

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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”

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Disposition

01 Problem in scope

02 Linked Building Data

03 The case

04 Implementation

05 Final Words

01Problem in Scope

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Overall information flow

Architect

Client

GeometrySpace function

Indoor Climaterequirements

Control strategy Control strategy

Indoor Climate & Low voltageEnergy engineer engineer

Electrician

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Data exchange of today

Architect

Client

GeometrySpace function

Indoor Climaterequirements

Control strategy Control strategy

Indoor Climate & Low voltageEnergy engineer engineer

Electrician

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Change management

Architect

Client

GeometrySpace function

Indoor Climaterequirements

Control strategy Control strategy

Indoor Climate & Low voltageEnergy engineer engineer

Electrician

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Architect

Client

GeometrySpace function

Indoor Climaterequirements

Control strategy Control strategy

Indoor Climate & Low voltageEnergy engineer engineer

Electrician

Change management

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Future scenario

Building ManagementSystem

Data processingKnowledge base

ActualconditionsFindings

Future buildingsControl strategy tweaks

Client

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Slidetitel

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How to integrate the architectural model with the engineer’s demands and the actual systems installed in the future building?

Linked Building Data

02

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Linked Building Data Community Group

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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#

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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

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BOTSelected relationships

bot:Elementbot:Zone

arch:spaceB arch:WC1arch:spaceA

bot:adjacentZone bot:containsElement

rdf:type rdf:typerdf:type

ABoxTBox

The case

03

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NavitasAarhus University, Denmark

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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 .

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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 .

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Sensor observationsextracted fromspreadsheets

Prototype:Tool for equippingduring design

ng-plan

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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

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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

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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

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DEMO

https://youtu.be/P_38gIvrbmg

Final words

05

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Conclusions

• Data model developed organically as project evolves

• Explicit information exchanges through all processes

• DEMO illustrates the ease of accessing the data

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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