Ocd arc energy_20160427

Sistemas de información energética para edificios y ciudades basados en tecnologías semánticas

Dr. Leandro MadrazoÁlvaro SiciliaARC Engineering and Architecture La SalleRamon Llull University, Barcelona, Spainwww.salleurl.edu/arc

OPENCITYDATA: Red temática española de Open Data y Ciudades

Inteligentes

Para mejorar la eficiencia energética de edificios y ciudades, los diversos actores implicados – técnicos, consultores, empresas y usuarios– requieren disponer de información de múltiples dominios –urbanístico, arquitectónico, energético, económico, social– que se encuentra distribuida en múltiples fuentes.

Las tecnologías semánticas permiten integrar estos datos en modelos energéticos para evaluar el comportamiento de los edificios y ciudades desde un punto de vista sistémico, y así poder tomar decisiones encaminadas a mejorar su rendimiento.

ARC: ARQUITECTURA, REPRESENTACIÓN y COMPUTACIÓN

• grupo multidisciplinar dedicado al diseño, desarrollo y aplicación de las tecnologías de la información y comunicación (TIC) a la arquitectura, creado en 1999.

• reconocido como grupo de investigación en la convocatoria SGR 2009 del AGAUR

• el grupo se ha consolidado en torno a las 15 personas, (investigadores, profesores, becarios) formadas en distintas áreas: arquitectura, ingeniería y diseño

• coordinador de proyectos nacionales y europeos

Currently, the lines of research of the group are:

•Design and construction: building information modeling (BIM), modular construction and manufacturing, simulation, design and construction processes, and component catalogues (product modeling).

•Energy information systems: energy information systems for buildings and urban environments using semantic technologies.

•Technology-enhanced learning: collaborative learning environments and digital libraries.

•Information spaces: interactive interface design, information visualization, concept maps and data mining.

2008-2011 IntUBE: Intelligent use of building’s energy information7th Framework Programme / Coordinator: VTT, Finland

2009-2012 RÉPENER: Control and improvement of energy efficiency in buildings through the use of repositories Spanish National RDI Plan / Coordinator: ARC Engineering and Architecture La Salle, Spain

2011-2014 SEMANCO: Semantic Tools for Carbon Reduction in Urban Planning7th Framework Programme / Coordinator: ARC Engineering and Architecture La Salle, Spain

2013-2016 OPTIMUS: Optimising the energy use in cities with smart decision support system7th Framework Programme / Coordinator: National Technical University of Athens, Greece

2015-2019 OPTEEMAL: Optimised Energy Efficient Design Platform for Refurbishment at District Level Horizon 2020 Programme / Coordinator: CARTIF, Spain

2014-2017 ENERSI: Energy service platform based on the integration of data from multiple sources Spanish National RDI Plan / Coordinator: Innovati Networks, Spain

Research projects on energy information systems:

IntUBE Intelligent use of building’s energy information2008-2011 / 7th Framework Programme

• VTT(Project Coordinator), FINLAND• CSTB Centre Scientifique et Technique du Bâtiment, FRANCE• TNO Netherlands Organisation for Applied Scientific Research,

NETHERLANDS• SINTEF Group, NORWAY• University of Teesside and Centre for Construction Innovation & Research,

UNITED KINGDOM• ARC Engineering and Architecture La Salle, Ramon Llull University, SPAIN• Università Politecnica delle Marche, ITALY • University College Cork, Department of Civil & Environmental Engineering ,

IRELAND• University of Stuttgart- Institute for Human Factors and Technology

Management, GERMANY• Vabi Software, NETHERLANDS• Pöyry Building Services Oy, FINLAND• Ariston Thermo Group, ITALY

EIIP – Energy Information Integration Platform

BIM server SIM server RD serverPIM server

Co

nce

pt

Desig

n d

eve

lop

.

Simulation tool

Building lifecycle

Co

ntr

ol /

main

ten

an

ce

Retr

ofi

t

desig

n

KNOWLEDGE

e.g. benchmark

Monitoring/BMS

INFORMATION

Capturing the energy information flow throughout the different stages of the whole building lifecycle

BIM

Static data (geometry, spaces, building systems)

Simulated energy performance data

Real monitored data (climate, occupancy)

Metadata to interlink repositories

Energy Information Integration Platform EIIP

PIM server

SIM server

BIM server

RD server

Distributed repositories

s

e

r

v

i

c

e

s

Climate

Monitoring

data

Building

data

Simulation

data

ENERGY INFORMATION CYCLE

DATA SOURCES

s

e

r

v

i

c

e

s

USERS

Energy

companies

Building

Owner

Building

Designer

Occupants

…

IntUBE – Energy Information Integration Platform

Extract benchmark

Monitoring

data

Performanceindicators

Demonstration scenario

Publicly subsidised

apartment building in

Cerdanyola del Vallès,

Barcelona. Contact sensors for opening status windows and doors

Temperature and relative humidity, inside, outside, air collector

Illuminance sensor for blind position detection

Touch Panel Screen

Hub connected to Internet

Boiler and heat exchanger SHW

Apartment 2.1

Apartment 2.2

S8S8

S7S7

S4S4

S6S6

S10S10S1S1

S5S5

S17S17 S15S15 S13S13S14S14

S18S18

S11S11

S12S12

FUNITEC (24 sensors)•Temperature: 7•Humidity: 7•State

•Blinds: 5•Windows: 5

CIMNE (32 sensors)•Temperature: 16•Pulse: 4•Energy Rate: 12

A demonstration scenario was implemented in a building where several sensors were installed and a screen to advise dwellers.

kg

0.150.15

kg

User interface installed in a social housing building to advise dwellers to reduce their energy consumption. Also, it shows current consumption of each apartment.

• An operative EIIP (Energy Information Integration Platform) working as NEXUS of energy data in all stages of the lifecycle:

1. Storing BIM models in a server (volumes/spaces in Revit) 2. Enriching BIM models with energy attributes 3. Storing simulation outputs with simulation software4. Integrating monitoring data (OPC server) in the EIIP

What was achieved in IntUBE:

RÉPENER Control and improvement of energy efficiency in buildings through the use of repositories 2009-2012 / Spanish National RDI plan

• ARC Engineering and Architecture La Salle, Ramon LlullUniversity (Project Coordinator) SPAIN

• Faculty of Business and Computer Science, HochschuleAlbstadt-Sigmaringen, GERMANY

The aim of this research project has been to design and implement a prototype of an energy information system using semantic technologies, following the philosophy of the Linked Open Data initiative.

LINKED DATA SOURCES

OFFLINE DATA SOURCES

Leako

CIMNE

Building Repository

Climate

…

Energy Model

Ontology Repository

SERVICES

Analysis

Visualization

Simulation

TOOLS

Prediction

GUI

Moving from a platform to a system of energy information with open and proprietary data linked using ontologies

System architecture

Building ontologies: A process to transfer knowledge from domain experts to ontology engineers- informal method, based on standards

Process

Certificate

BuildingDomain

icaen:certificates

ProjectData Literal : Stringicaen:ID_LOCALITAT

icaen:hasProject

WeatherStation

Point

rdfs:labelaemet:stationName

Literal : String Literal : String

geo:Location

geo:lat

geo:long

Literal : DecimalLiteral : Decimal

Town

geo:latgeo:long

Literal : Decimal Literal : Decimal

City Village

rdfs:label

Literal : string

rdfs:label

Literal : string

rdfs:label

Literal : string

Place

rdfs:subClassOf rdfs:subClassOf

rdfs:subClassOf

lgd:population

Literal : Decimal

ICAEN ontology

AEMET ontology Linked GeoDataontology

aemet:Temperature

Literal : Decimal

Excerpts of local ontologies developed in OWL language.

Certificate

BuildingDomain

icaen:certificates

ProjectData Literal : Stringicaen:ID_LOCALITAT

icaen:hasProject

WeatherStation

Point

rdfs:labelaemet:stationName

Literal : String Literal : String

geo:Location

geo:lat

geo:long

Literal : DecimalLiteral : Decimal

Town

geo:latgeo:long

Literal : Decimal Literal : Decimal

City Village

rdfs:label

Literal : string

rdfs:label

Literal : string

rdfs:label

Literal : string

Place

rdfs:subClassOf rdfs:subClassOf

rdfs:subClassOf

lgd:population

Literal : Decimal

aemet:Temperature

Literal : Decimal

Located

closeTo

ICAEN ontology

AEMET ontology Linked GeoDataontology

Located

Mappings between ontologies are created to interrelate data sources allowing integrated queries.

Knowledge discovery process (we use tools like SILK for finding relationships)

Virtuoso Server

SPARQL Endpoint

Microsoft Access

Spanish gazetteer

Paradox

Leako

Spreadsheet

ICAEN

Data portal (Pubby)

RÉPENER

Web site

ETL process

Data integration process

www.seis-system.org

• Integration of data from multiple sources using Semantic Web technologies

• Taxonomy of energy related data• Ontology representing a building energy model• On-line application focused on specific user profiles

What was achieved in RÉPENER:

ENERSI: Energy service platform based on the integration of data from multiple sources

2014-2017 / Spanish National RDI plan

• Innovati Networks, SPAIN (Project Coordinator)

• NIMBEO, SPAIN

• ARC Engineering and Architecture La Salle, Ramon LlullUniversity, SPAIN

• Universidad Carlos III de Madrid, SPAIN

A continuation of RÉPENER project to build an open integrated service platform for energy consultancy companies, public administration, manufacturers…

More data available:

- ICAEN: Energy certificates up to 400.000 records- Sant Cugat city: consumption of 80 public buildings

(monthly)- IDAE: consumption data of 8000 public buildings

(yearly)

More services available:

- Custom services for companies, consultants based on the integrated data

Examples of the platform services

Migration from relational DB to Virtuoso

MapSchema

Map-On MorphData

UploaderGeneric

DBVirtuoso

Input:

•Database connection

Output:

•XML file with Database Schema

Input:

•Mappings

•Database connection

Output:

•Database dump in RDF

Input:

•Database Schema or SQL

•Ontology

Output:

•Mappings file

Input:

•Database dump file

•Virtuoso connection

•DataLayer Lib

Output:

•Upload data to virtuoso

•Run tests

Set of functions to interact with VirtuosoInput:• Virtuoso connection• Ontology .jar

DataLayer Lib

Maps ontology to java classesInput:• OntologyOutput:• Ontology .jar

OWL Compiler

Map-On

morph-RDBRDB

Click-OnO

Domain

AutoMap4OBDA

MAutoMap

M

User

Migration from relational DB to Virtuoso

SEMANCO Semantic Tools for Carbon Reduction in Urban Planning2011-2014 / 7th Framework Programme

• Engineering and Architecture La Salle, Ramon Llull University, (Project Coordinator), SPAIN

• University of Teesside and Centre for Construction Innovation & Research, UNITED KINGDOM

• CIMNE, International Center for Numerical Methods in Engineering, SPAIN• Politecnico di Torino, ITALY• Faculty of Business and Computer Science, Hochschule Albstadt-

Sigmaringen, GERMANY• Agency9 AB, SWEDEN• Ramboll, DENMARK• NEA National Energy Action, UNITED KINGDOM• FORUM, SPAIN

SEMANCO’s purpose was to provide a semantic-based platform to help different stakeholders involved in urban planning (architects, engineers, building managers, local admnistrators, citizens and policy makers) to make informed decisions about how to reduced carbon emissions in cities.

Cities are complex systems made up of physical elements –

buildings and streets, energy supply and communication

infrastructures – in which multiple actors –citizens, professionals–

interact to carry out activities which put into relation the multiple

dimensions of the system –economic development with

transportation networks, energy consumption with buildings energy

performance.

The problem of carbon emission reduction in urban areas

cannot be constrained to a particular geographical area or scale, nor

is it the concern of a particular discipline or expert: it is a systemic

problem which involves multiple scales and domains and the

collaboration of experts from various fields.

Urban energy systems are “the combined process of acquiring and

using energy to satisfy the demands of a given urban area”

(Keirstead and Shah, 2013).

Models are created to assess the performance of an urban

system in a particular domain (building, transport, energy), or in a

combination of them. These models are abstractions of the physical

structure of the city, simplified representations of what the city actually

is. Most important, models should grasp the activity of an urban

system: the elements that come into play with a particular purpose,

the interactions among them.

An energy system model is “a formal system that represents the

combined processes of acquiring and using energy to satisfy the

energy service demands of a given urban area” (Keirstead et al.,

2012).

The goal of SEMANCO has been to create models of urban energy

systems:

- to understand the current state of the system

- to help to take decisions to influence its future evolution

Semantic technologies are used:

1. To integrate data from different sources (cadastre, GIS,

carbon emission, energy need) and domains (urban planning, energy

efficiency, economics)

2. To facilitate the interoperability between the combined data

and energy assessment and analysis tools

Semantic-based models of an urban energy system embody the

combined knowledge of the experts which analyze a complex

problem from multiple perspectives. Such models are not just a

representation of a reality, but a representation of a complex reality as

conceptualised by experts.

Building repositories

Energydata

Environmentaldata

Economicdata

Enabling scenarios for stakeholders

Building stock energy modelling

tool

Advanced energy information

analysis tools

Interactivedesign tool

Energy simulationand trade-off tool

Policy Makers CitizensDesigners/Engineers Building ManagersPlanners

Regulations Urban Developments Building OperationsPlanning strategies

TechnologicalPlatform

SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF)

CO2 emissions reduction!

Application domains

Stakeholders

SEMANCO Integrated Platform

DATA (Distributed and

heterogeneous)SEIF

Semantic Energy

Model

(global ontology)

URBAN ENERGY

MODELS

Data ToolsUsers

TOOLS

Private

Open

LOD

ApplicationsExternal

Embedded

Interfaced

Data connected through the Semantic Energy Information Framework

OPEN SEMANTIC DATA MODELS

DATA TOOLS

Home Case Studies Analyses Data Services About

Newcastle United Kingdom

Legend

Source:

Indicator:

Units: - m2 year- year

Scale: - District- Building

Filters

54000

CO2 Emissions (tCO2 year)

213F

SAP Rate (u.)

G

Tenure

Private owner1234567

Energy demand (kj. year)

234210

Index of multiple deprivation(u)

3

Apply filters

Reset filters

Number of buildings: 15322 / 50200

Total surface built: 9023 / 34342 m2

Urban indicators

Age average of building stock: 77 / 42 years

Index of multiple deprivation: 4 / 15

Income score: 53 / 52

District indicators

Fuel poverty: 90 / 20 %

CO2 Emissions (tCO2 year): 234 / 3243.

Energy Consumption: 34342 / 23423

Performance indicators

Energy demand: 2343 / 234

SAP rate: 24 / 54

….

…..

Table3D Map

ProjectionCurrent status

Relationship

Building 1

Building use: Single-family houseSurface: 4234Height: 23Floors: 5

CO2 emissions: 23523Energy consumption: 4234Energy demand: 32423SAP: 2345

IMD: 12Fuel poverty: 42%Income index: 32

LinkExport

intervention

SEIF + Semantic

energymodel

SEMANCO INTEGRATED PLATFORM

Urban Energy Model A

- Data: Consumption- Tools: Simulation (Ursos)- Users: Energy consultants

- Plans: Projects

- Data: Building properties- Tools: Assessment (SAP)- Users: Planners, City

- Plans: Projects

Experts’ knowledgecaptured in theontologies

RDF data (semantic data)

Urban energy model (GIS enriched with semanticdata)

Experts’sknowledgedescribe in Use Case and Activitiestemplates

Repositories(linked data ornon-structureddata) of energyrelated data

Urban Energy Model B

Urban Energy System

Integration of multiple data and knowledge in a platform which enables the creation of energy models of an urban energy system

To determine the baseline (energy performance based on the available data and tools) of an urban area

1

To create plans and projects to improve the existing conditions

2

To evaluate projects3

C L U S T E R V I E WTA B L E V I E W

P E R F O R M A N C E I N D I C AT O R S F I LT E R I N G

M U LT I P L E S C A L E V I S U A L I Z AT I O N

Once a baseline reflecting the current state of the urban energy model has beencreated, different visualization tools can be used to identify problem areas.

INTEGRATED PLATFORM : URBAN ENERGY MODEL: BASELINE

Visualizing the energy information at the neighborhood level

Smart City Expo World Congress, Barcelona, 18-20 November 2014Visualization of energy information at the building level

INTEGRATED PLATFORM : URBAN ENERGY MODEL: BASELINE

Smart City Expo World Congress, Barcelona, 18-20 November 2014information concerning the selected building derived from the integrated semantic model

Building geometry obtained from the3D model

Street address obtained fromGoogle Geolocation services

Performance values to becalculated with energyassessment tool

Year of construction obtained fromthe cadastre

Smart City Expo World Congress, Barcelona, 18-20 November 2014Interface of the URSOS tool. The input data is automatically filled thanks to the semanticintegration of different data sources. Users can modify the input data in case there are errors.

Interface of the URSOS tool. The input data is automatically filled thanks to the semanticintegration of different data sources. Users can modify the input data in case there are errors.

Wall, ground and roofproperties from the buildingtypologies database

Year of construction from the Cadastre

Geometry obtained from the 3D model

Street address nameand Street view fromGoogle Geolocationservices

Ventilation from the buildingtypologies database

Results of the energy simulation carried out by URSOS

Creating plans to improve energy efficiency of buildings

Selecting buildings which belong to the plan at stake. They have been spotted before with the baseline assessment tools.

Projects to apply improvement measures

Current status of the buildings before applying measures

Applying improvements. For example, renovating the existing windows or replacing them with new ones

Results after applying the improvement measures

Smart City Expo World Congress, Barcelona, 18-20 November 2014

Projects can be compared with a multi-criteria decision tool included in the platform. Users can select the weight (importance) of the performance indicators. Besides, other indicators defined by

users can be included in the analysis, for example: foreseen funding.

Smart City Expo World Congress, Barcelona, 18-20 November 2014The results of the multi-criteria analysis: in green color the best

choices.

The main goal of the demonstration in the Manresa case

study is to assess the effectiveness of the measures to

refurbish buildings in two neighbourhoods.

The users (Architect, Industrial Engineer, Engineer, Urban

Planner) evaluate the impact of the energy efficiency on the

building by using the URSOS simulating software tool

integrated in the platform.

Data sources: Cadastre, census, socio-economic, building

typologies(u-values, windows properties, systems…)

Three different projects were assessed:

• Building envelope: upgrading windows

• Heating system improvement: acquiring new high efficient

boilers

• Use of renewable energies: installing energy generation

systems fed with renewable sources.

Smart City Expo World Congress, Barcelona, 18-20 November 2014

DEMONSTRATION SCENARIO: MANRESA, SPAIN

The main goal of the demonstration in the Newcastle case

study is to identify housing buildings with a high risk of fuel

poverty and to propose measure to upgrade them.

An Energy Consultant has been contracted by Newcastle

City Council to come up with scenarios to improve low energy

efficient dwellings in the Kenilworth Road area which is

currently amongst the worst performing streets in Newcastle

upon Tyne.

Data sources: Lower Level Super Output Area (LLSOA):

income, fuel poverty, Index of multiple deprivation.


• Insulation based refit

• Renewables refit

• Targeted fabric refit

DEMONSTRATION SCENARIO: NEWCASTLE, UK

The main goal of the demonstration in the Copenhagen case

study is to assess different strategies regarding supply of

energy, based both on central and distributed solutions in a

greenfield planning situation.

An urban planner from the Environmental Department of the

Municipality has been assigned the task to evaluate new

strategies currently being debated by local authorities. One of

them is to change energy supplied by heat pumps.

Data sources: building typologies (supply technologies,

energy demand), carbon emission coefficients.


• District heating projection

• Individual fossil fuel solutions

• Ground source heat pump

DEMONSTRATION SCENARIO: COPENHAGEN, DENMARK

DEMONSTRATION SCENARIO: TORINO, ITALY

SERVICE PLATFORM TO SUPPORT PLANNING OF ENERGY EFFICIENT CITIES

An energy service platform that supports planners, energy consultants, policy makers

and other stakeholders in the process of taking decisions aimed at improving the energy

efficiency of urban areas.

The services provided are based on the integration of available energy related data from

multiple sources such as geographic information, cadastre, economic indicators, and

consumption, among others.

The integrated data is analysed using assessment and simulation tools that are

specifically adapted to the needs of each case.

www.eecities.com

www.semanco-tools.eu

A platform which enables expert users to create energy models of urban areas to assess the current peformance of buildings and todevelop plans and projects to improve the current conditions, including:

• An ontology for energy modeling in urban areas • A methodology to integrate data from multiple domains and

disciplines • A set of tools to support ontology design (Click-On, Map-On)• An operative platform which can be implemented in other

cities

What was achieved in SEMANCO:

URSOS Energy calculation engine

GIS data

Census CadastreClimate

Typology Socio-Economic

Energy-related data Semantic Energy Information Framework

Integrated Platform

ELITEFederation engine

OntologyOWL-DL liteA

URSOS Input form3D Maps

1

2

3 5

4

ONTOLOGY DESIGN TOOLS


ONTOLOGY DESIGN TOOLS: Click-On

©Faculty of Business and Computer Science, Hochschule Albstadt-Sigmaringen, GERMANY

ONTOLOGY DESIGN TOOLS: Map-On

Relational database

Domain ontology

AutoMap4OBDA

R2RML mappings that can be modified and improved in Map-On

ONTOLOGY DESIGN TOOLS: AutoMap4OBDA

OPTIMUS Optimising the energy use in cities with smart decision support system

2013-2016 / 7th Framework Programme

• National Technical University Athens (Project Coordinator), GREECE• Engineering and Architecture La Salle, Ramon Llull University, SPAIN• ICLEI, GERMANY• TECNALIA, SPAIN• D’APPOLONIA, ITALY• Politecnico di Torino, ITALY• Università deggli Studi di Genova, ITALY• Sense One Technologies Solutions, GREECE• Commune di Savona, ITALY• Gemeente Zaanstad, THE NETHERLANDS• Ajuntament de Sant Cugat del Vallès, SPAIN

The purpose of OPTIMUS is to develop a semantic-based decision support system which integrates data from five different types / sources: climate, building operation, energy production costs, energy consumption, user’s feedback.

The OPTIMUS DSS will be tested in three

municipalities across Europe:

• Savona, ITALY

• Sant Cugat del Vallès, SPAIN

• Zaanstad, THE NETHERLANDS

Semantic framework

Weather

forecasting

De-centralized

sensor-based

Feedback from

occupants

Energy

prices

RES

production

DSS INTERFACE

Sant Cugat

Savona

Zaanstad

The results of the implementation of the actions in each pilot city will modify the data sources.

IMPLEMENTATION

PREDICTION MODELS

DSS ENGINE

INFERENCE RULES

The inference rules and predictionmodels are implemented in the DSSengine

Historical data

Predicted data

Monitored data

Relations between inputdata (real time andpredicted data, and staticuser inputs) for suggestingan action plan

ACTION PLANS

OPTIMUS DSS

City dashboard

OPTIMUS DSS

Building dashboard

OPTIMUS DSS

Monitored data

Optimization of the boost time of the heating/cooling system

Optimization of selling/consumption of electricity produced by a PV system

Triple store

(Virtuoso Server)

Semantic Framework

Weather data

De-centralizeddata

Feedbackoccupants

Ztreamy

ServerEnergy prices

Energyproduction

Semantic

Service

RAW

DATA

RDF DATA:

RAW DATA + MEANING

RDF DATA +

CONTEXT

INTEGRATED

DATA

Data capturing modulesSources

OPTIMUS DSS

1. Data

translation

2. Data

communication

3. Data

contextualization

4. Data

storage

publishers

Subscriber

DSS

Third-parties

SEMANTIC INTEGRATION PROCESS

Data source

Publisher

Ztreamyserver

Semanticservice

Virtuoso triple store

WP2 Data capturing modules

WP3 Semantic Framework

WP3Optimus DSS

Subscriber

Data capturing module T3.2, T3.3

DSS Engine

T3.4DSS interfaces


Data source

Publisher

Ztreamyserver

Semanticservice




WP3Optimus DSS

Subscriber


DSS Engine

T3.4DSS interfaces

RDF template for data capturing modules

RDF DATA

Raw data


RDF data from modules + context data

+ + +Data source

Publisher

Ztreamyserver

Semanticservice




WP3Optimus DSS

Subscriber


DSS Engine

T3.4DSS interfaces

RDF DATA

RDF DATA + CONTEXT

Raw data


2. Implement integration methods based on pub/sub systems (e.g. Ztreamy)

Data source

RDF data from modules + contextual data

+ + +Data source

Publisher

Ztreamyserver

Semanticservice




WP3Optimus DSS

Subscriber


DSS Engine

T3.4DSS interfaces

OPTIMUS ONTOLOGY

- Static data (Building and systems features) can be modelled with an ontology extended from Semanco ontology (Polito and Funitec already worked on that) http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl

- Dynamic data (sensoring) can be modelled with an ontology which extends Semantic Sensor Network (SSN) ontology http://purl.oclc.org/NET/ssnx/ssn

Sensors(based on SSN ontology)

Optimus ontology

Building & systems features(based on Semanco ontology)

http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl

http://purl.oclc.org/NET/ssnx/ssn

Ontologies

ssn:Sensor

ssn:SensingDevice

ssn:Observation

optimus:SunnyPortal_EnergyProduction

semanco:Solar_Irradiationssn:FeatureOfInterest

ssn:Property

ssn:System

subClassOf

subClassOf

ssn:hasSubSystem

ssn:observes

ssn:observes

subClassOf

ssn:hasProperty

subClassOf

ssn:observedBy

subClassOf

ssn:featureOfInterest

ssn:observedProperty

semanco:PVSystem_Peak_Power

optimus:SunnyPortal_SolarRadiation

subClassOf

ssn:SensorOutput

ssn:observationResult

ssn:hasValue

time:Instant

ssn:observationResultTime

time:inXSDDateTime

literal

ssn:Platform

ssn:Deploymentssn:deployedOnPlatform

ssn:hasDeployment

sumo:located

sumo:Building

sumo:Room

Semanco:Space_Heating_System

Semanco:Ventilation_System

…

literal

subClassOf

optimus:Solar_IrradiationSensorOutput

optimus:PVSystem_Peak_PowetSensorOutput

subClassOf

ssn:onPlatform

optimus:SunnyPortal

subClassOf

subClassOfssn:observes

optimus:Solar_IrradiationFeature

subClassOf

optimus:PVSystem_Peak_PowerFeature

ssn:hasProperty

Static part of the ontology Building and System features

Semantic Sensor Network

OPTIMUS

SEMANCO

OPTIMUS ONTOLOGY

Sant Cugat Savona Zaanstad

- Weather forecast: 9 5 9

- De-centralized data: 204 64 283

- Feedback occupants: 2 2 1

- Energy prices: 3 4 0

- RES production: 2 2 0

Current status of data streams:

• The SEMANCO ontology has been expanded with dynamic data: The OPTIMUS ontology includes indicators such as energy consumption and CO2 emissions, climate and socio-economic factor influencing consumption

• A front-end application to predict the building performance based on the prediction models is being implemented in three cities (Zaanstad, Savona, Sant Cugat)

What is being done in OPTIMUS:

OPTEEMAL: Optimised Energy Efficient Design Platform for Refurbishment at District Level

2015-2019 / Horizon 2020 Programme

• Fundación CARTIF (Project Coordinator), SPAIN• Fundación TECNALIA, SPAIN• Nobatek, FRANCE• ARC Engineering and Architecture La Salle, Ramon Llull University, SPAIN• Technical University of Crete, GREECE• ACCIONA Infraestructuras, SPAIN• United Technologies Research Centre, IRELAND• Expert System, ITALY• ARGEDOR Bilişim Teknolojileri, TURKEY• Distretto tecnologico trentino per l’energia e l’ambiente, ITALY• Fomento San Sebastián, SPAIN• Lunds Kommun, SWEDEN• Steinbeis Innovation gGmbH, GERMANY

OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design

SWIMing VoCamp Workshop | Dublin, 22–23 March 2016

District Data Model

Contextual data

Socio-economic data

Weather data

Energy prices

Users’ objectives

Monitoring data

IFC model

CityGML

IPD Platform Users

Insert



Simulation Data models

District Data Model

Contextual data

Socio-economic data

Weather data

Energy prices

Users’ objectives

Monitoring data

IFC model

CityGML

…Energy model

Economic model n model

IPD Platform Users

Insert



Simulation Data models

District Data Model

Contextual data

Socio-economic data

Weather data

Energy prices

Users’ objectives

Monitoring data

IFC model

CityGML

…Energy model


IPD Platform Users

DPIs calculation and Scenario optimization

Insert

BASELINE

Estimation of the performance

* DPI = District Performance

Indicator



Simulation Datamodels

District Data Model – Scenario Generation

Contextual data

Socio-economic data

Weather data

Energy prices

Users’ objectives

Monitoring data

IFC model

CityGML

…Energy model


IPD Platform Users

Energy Conservation Measures (ECMs) catalogue

Select

DPIs calculation and Scenario optimization

Insert

Ocd arc energy_20160427

Technology

Transcript of Ocd arc energy_20160427