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Sistemas de información energética
para edificios y ciudades basados en
tecnologías semánticas
Dr. Leandro Madrazo
Álvaro ...
Para mejorar la eficiencia energética de edificios y
ciudades, los diversos actores implicados – técnicos,
consultores, em...
ARC: ARQUITECTURA, REPRESENTACIÓN y COMPUTACIÓN
• grupo multidisciplinar dedicado al diseño, desarrollo y
aplicación de la...
Currently, the lines of research of the group are:
•Design and construction: building information modeling
(BIM), modular ...
2008-2011 IntUBE: Intelligent use of building’s energy information
7th Framework Programme / Coordinator: VTT, Finland
200...
IntUBE Intelligent use of building’s energy information
2008-2011 / 7th Framework Programme
• VTT(Project Coordinator), FI...
EIIP – Energy Information Integration Platform
BIM server SIM server RD serverPIM server
Concept
Designdevelop.
Simulation...
Energy Information Integration
Platform EIIP
PIM server
SIM server
BIM server
RD server
Distributed repositories
s
e
r
v
i...
Demonstration scenario
Publicly subsidised
apartment building in
Cerdanyola del Vallès,
Barcelona. Contact sensors for ope...
kg
0.150.15
kg
User interface installed in a social housing building to advise dwellers to reduce
their energy consumption...
• An operative EIIP (Energy Information Integration Platform)
working as NEXUS of energy data in all stages of the lifecyc...
RÉPENER Control and improvement of energy efficiency
in buildings through the use of repositories
2009-2012 / Spanish Nati...
The aim of this research project has been to design
and implement a prototype of an energy information
system using semant...
LINKED DATA SOURCES
OFFLINE DATA SOURCES
Leako
CIMNE
Building Repository
Climate
…
Energy Model
Ontology Repository
SERVIC...
Building ontologies: A process to transfer knowledge from domain
experts to ontology engineers- informal method, based on ...
Certificate
BuildingDomain
icaen:certificates
ProjectData Literal : Stringicaen:ID_LOCALITAT
icaen:hasProject
WeatherStati...
Certificate
BuildingDomain
icaen:certificates
ProjectData Literal : Stringicaen:ID_LOCALITAT
icaen:hasProject
WeatherStati...
Virtuoso Server
SPARQL Endpoint
Microsoft Access
Spanish gazetteer
Paradox
Leako
Spreadsheet
ICAEN
Data portal
(Pubby)
RÉP...
www.seis-system.org
www.seis-system.org
• Integration of data from multiple sources using Semantic Web
technologies
• Taxonomy of energy related data
• Ontology r...
ENERSI: Energy service platform based on the integration
of data from multiple sources
2014-2017 / Spanish National RDI pl...
A continuation of RÉPENER project to build an open
integrated service platform for energy consultancy
companies, public ad...
Examples of the platform services
Migration from relational DB to Virtuoso
Map
Schema
Map-On Morph
Data
Uploader
Generic
DB
Virtuoso
Input:
•Database
connec...
Map-On
morph-RDBRDB
Click-On
O
Domain
AutoMap4OBDA
M
AutoMap
M
User
Migration from relational DB to Virtuoso
SEMANCO Semantic Tools for Carbon Reduction in
Urban Planning
2011-2014 / 7th Framework Programme
• Engineering and Archit...
SEMANCO’s purpose was to provide a semantic-based
platform to help different stakeholders involved in
urban planning (arch...
Cities are complex systems made up of physical elements –
buildings and streets, energy supply and communication
infrastru...
Models are created to assess the performance of an urban
system in a particular domain (building, transport, energy), or i...
Semantic technologies are used:
1. To integrate data from different sources (cadastre, GIS,
carbon emission, energy need) ...
Building
repositories
Energy
data
Environmental
data
Economic
data
Enabling scenarios for stakeholders
Building stock
ener...
SEMANCO Integrated Platform
DATA
(Distributed and
heterogeneous)
SEIF
Semantic Energy
Model
(global ontology)
URBAN ENERGY...
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
...
To determine the baseline (energy
performance based on the available
data and tools) of an urban area
1
To create plans an...
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...
INTEGRATED PLATFORM : URBAN ENERGY MODEL: BASELINE
Visualizing the energy information at the neighborhood level
Smart City Expo World Congress, Barcelona, 18-20 November 2014
Visualization of energy information at the building level
I...
Smart City Expo World Congress, Barcelona, 18-20 November 2014
information concerning the selected building derived from t...
Smart City Expo World Congress, Barcelona, 18-20 November 2014
Interface of the URSOS tool. The input data is automaticall...
Interface of the URSOS tool. The input data is automatically filled thanks to the semantic
integration of different data s...
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 too...
Smart City Expo World Congress, Barcelona, 18-20 November 2014
The results of the multi-criteria analysis: in green color ...
The main goal of the demonstration in the Manresa case
study is to assess the effectiveness of the measures to
refurbish b...
The main goal of the demonstration in the Newcastle case
study is to identify housing buildings with a high risk of fuel
p...
The main goal of the demonstration in the Copenhagen case
study is to assess different strategies regarding supply of
ener...
DEMONSTRATION SCENARIO: TORINO, ITALY
SERVICE PLATFORM TO SUPPORT PLANNING OF ENERGY EFFICIENT CITIES
An energy service platform that supports planners, energy ...
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...
URSOS Energy
calculation engine
GIS data
Census CadastreClimate
Typology Socio-Economic
Energy-related data Semantic Energ...
www.semanco-tools.eu
ONTOLOGY DESIGN TOOLS: Click-On
©Faculty of Business and Computer Science, Hochschule Albstadt-Sigmar...
www.semanco-tools.eu
ONTOLOGY DESIGN TOOLS: Map-On
©ARC Engineering and Architecture La Salle, SPAIN
ONTOLOGY DESIGN TOOLS: Map-On
Relational
database
Domain
ontology
AutoMap4OBDA
R2RML mappings that
can be modified and
improved in Map-On
ONTOLOGY DESIG...
OPTIMUS Optimising the energy use in cities with smart
decision support system
2013-2016 / 7th Framework Programme
• Natio...
The purpose of OPTIMUS is to develop a semantic-
based decision support system which integrates data
from five different t...
The OPTIMUS DSS will be tested in three
municipalities across Europe:
• Savona, ITALY
• Sant Cugat del Vallès, SPAIN
• Zaa...
Semantic framework
Weather
forecasting
De-centralized
sensor-based
Feedback from
occupants
Energy
prices
RES
production
DS...
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-centralized
data
Feedback
occupants
Ztreamy
Server
Energ...
Data source
Publisher
Ztreamy
server
Semantic
service
Virtuoso
triple store
WP2
Data capturing modules
WP3
Semantic Framew...
Data source
Publisher
Ztreamy
server
Semantic
service
Virtuoso
triple store
WP2
Data capturing modules
WP3
Semantic Framew...
RDF data from modules + context data
+ + +
Data source
Publisher
Ztreamy
server
Semantic
service
Virtuoso
triple store
WP2...
2. Implement integration methods based on pub/sub systems (e.g. Ztreamy)
Data source
RDF data from modules + contextual da...
OPTIMUS ONTOLOGY
- Static data (Building and systems features) can be modelled with an ontology
extended from Semanco onto...
Ontologies
ssn:Sensor
ssn:SensingDevice
ssn:Observation
optimus:SunnyPortal_EnergyProduction
semanco:Solar_Irradiationssn:...
Sant Cugat Savona Zaanstad
- Weather forecast: 9 5 9
- De-centralized data: 204 64 283
- Feedback occupants: 2 2 1
- Energ...
• The SEMANCO ontology has been expanded with dynamic
data: The OPTIMUS ontology includes indicators such as
energy consum...
OPTEEMAL: Optimised Energy Efficient Design Platform
for Refurbishment at District Level
2015-2019 / Horizon 2020 Programm...
OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design
SWIMing VoCamp Workshop...
OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design
SWIMing VoCamp Workshop...
OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design
SWIMing VoCamp Workshop...
OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design
SWIMing VoCamp Workshop...
©ARC Engineering and Architecture La Salle
www.salleurl.edu/arc
arc@salleurl.edu
Ocd arc energy_20160427
Ocd arc energy_20160427
Ocd arc energy_20160427
Ocd arc energy_20160427
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Ocd arc energy_20160427

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Presentation of the research work of the group ARC Engineering and Architecture La Salle about energy information systems for buildings and cities based on semantic technologies. The presentation was given at the Universidad de Deusto, Bilbao, on 27 April, 2016, as part of the activities of the Opencitydata thematic network.

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Ocd arc energy_20160427

  1. 1. Sistemas de información energética para edificios y ciudades basados en tecnologías semánticas Dr. Leandro Madrazo Álvaro Sicilia ARC Engineering and Architecture La Salle Ramon Llull University, Barcelona, Spain www.salleurl.edu/arc OPENCITYDATA: Red temática española de Open Data y Ciudades Inteligentes
  2. 2. 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.
  3. 3. 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
  4. 4. 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.
  5. 5. 2008-2011 IntUBE: Intelligent use of building’s energy information 7th 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 Planning 7th Framework Programme / Coordinator: ARC Engineering and Architecture La Salle, Spain 2013-2016 OPTIMUS: Optimising the energy use in cities with smart decision support system 7th 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:
  6. 6. IntUBE Intelligent use of building’s energy information 2008-2011 / 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
  7. 7. EIIP – Energy Information Integration Platform BIM server SIM server RD serverPIM server Concept Designdevelop. Simulation tool Building lifecycle Control/ maintenance Retrofit design 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
  8. 8. 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 Performance indicators
  9. 9. 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 S10S10 S1S1 S5S5 S17S17 S15S15 S13S13 S14S14 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.
  10. 10. 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.
  11. 11. • 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 software 4. Integrating monitoring data (OPC server) in the EIIP What was achieved in IntUBE:
  12. 12. 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 Llull University (Project Coordinator) SPAIN • Faculty of Business and Computer Science, Hochschule Albstadt-Sigmaringen, GERMANY
  13. 13. 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.
  14. 14. 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
  15. 15. Building ontologies: A process to transfer knowledge from domain experts to ontology engineers- informal method, based on standards Process
  16. 16. Certificate BuildingDomain icaen:certificates ProjectData Literal : Stringicaen:ID_LOCALITAT icaen:hasProject WeatherStation Point rdfs:label aemet:stationName Literal : String Literal : String geo:Location geo:lat geo:long Literal : Decimal Literal : Decimal Town geo:lat geo: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 GeoData ontology aemet:Temperature Literal : Decimal Excerpts of local ontologies developed in OWL language.
  17. 17. Certificate BuildingDomain icaen:certificates ProjectData Literal : Stringicaen:ID_LOCALITAT icaen:hasProject WeatherStation Point rdfs:label aemet:stationName Literal : String Literal : String geo:Location geo:lat geo:long Literal : Decimal Literal : Decimal Town geo:lat geo: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 GeoData ontology Located Mappings between ontologies are created to interrelate data sources allowing integrated queries.  Knowledge discovery process (we use tools like SILK for finding relationships)
  18. 18. Virtuoso Server SPARQL Endpoint Microsoft Access Spanish gazetteer Paradox Leako Spreadsheet ICAEN Data portal (Pubby) RÉPENER Web site ETL process Data integration process
  19. 19. www.seis-system.org
  20. 20. www.seis-system.org
  21. 21. • 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:
  22. 22. 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 Llull University, SPAIN • Universidad Carlos III de Madrid, SPAIN
  23. 23. 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
  24. 24. Examples of the platform services
  25. 25. Migration from relational DB to Virtuoso Map Schema Map-On Morph Data Uploader Generic DB Virtuoso 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 Virtuoso Input: • Virtuoso connection • Ontology .jar DataLayer Lib Maps ontology to java classes Input: • Ontology Output: • Ontology .jar OWL Compiler
  26. 26. Map-On morph-RDBRDB Click-On O Domain AutoMap4OBDA M AutoMap M User Migration from relational DB to Virtuoso
  27. 27. SEMANCO Semantic Tools for Carbon Reduction in Urban Planning 2011-2014 / 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
  28. 28. 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.
  29. 29. 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).
  30. 30. 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
  31. 31. 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.
  32. 32. Building repositories Energy data Environmental data Economic data Enabling scenarios for stakeholders Building stock energy modelling tool Advanced energy information analysis tools Interactive design tool Energy simulation and trade-off tool Policy Makers CitizensDesigners/Engineers Building ManagersPlanners Regulations Urban Developments Building OperationsPlanning strategies Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders
  33. 33. SEMANCO Integrated Platform DATA (Distributed and heterogeneous) SEIF Semantic Energy Model (global ontology) URBAN ENERGY MODELS Data ToolsUsers TOOLS Private Open LOD Applications External Embedded Interfaced
  34. 34. Data connected through the Semantic Energy Information Framework OPEN SEMANTIC DATA MODELS DATA TOOLS
  35. 35. 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 owner 1234567 Energy demand (kj. year) 234210 Index of multiple deprivation(u) 3 Apply filters Reset filters Number of buildings: 15322 / 50200 Total surface built: 9023/ 34342m2 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 house Surface: 4234 Height: 23 Floors: 5 CO2 emissions: 23523 Energyconsumption: 4234 Energy demand: 32423 SAP: 2345 IMD: 12 Fuel poverty: 42% Income index: 32 LinkExport intervention SEIF + Semantic energy model 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’ knowledge captured in the ontologies RDF data (semantic data) Urban energy model (GIS enriched with semantic data) Experts’s knowledge describe in Use Case and Activities templates Repositories (linked data or non-structured data) of energy related 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
  36. 36. 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 projects 3
  37. 37. 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 been created, different visualization tools can be used to identify problem areas.
  38. 38. INTEGRATED PLATFORM : URBAN ENERGY MODEL: BASELINE Visualizing the energy information at the neighborhood level
  39. 39. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Visualization of energy information at the building level INTEGRATED PLATFORM : URBAN ENERGY MODEL: BASELINE
  40. 40. Smart City Expo World Congress, Barcelona, 18-20 November 2014 information concerning the selected building derived from the integrated semantic model Building geometry obtained from the 3D model Street address obtained from Google Geolocation services Performance values to be calculated with energy assessment tool Year of construction obtained from the cadastre
  41. 41. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Interface of the URSOS tool. The input data is automatically filled thanks to the semantic integration of different data sources. Users can modify the input data in case there are errors.
  42. 42. Interface of the URSOS tool. The input data is automatically filled thanks to the semantic integration of different data sources. Users can modify the input data in case there are errors. Wall, ground and roof properties from the building typologies database Year of construction from the Cadastre Geometry obtained from the 3D model Street address name and Street view from Google Geolocation services Ventilation from the building typologies database
  43. 43. Results of the energy simulation carried out by URSOS
  44. 44. Creating plans to improve energy efficiency of buildings
  45. 45. Selecting buildings which belong to the plan at stake. They have been spotted before with the baseline assessment tools.
  46. 46. Projects to apply improvement measures
  47. 47. Current status of the buildings before applying measures
  48. 48. Applying improvements. For example, renovating the existing windows or replacing them with new ones
  49. 49. Results after applying the improvement measures
  50. 50. 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.
  51. 51. Smart City Expo World Congress, Barcelona, 18-20 November 2014 The results of the multi-criteria analysis: in green color the best choices.
  52. 52. 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
  53. 53. 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. Three different projects were assessed: • Insulation based refit • Renewables refit • Targeted fabric refit DEMONSTRATION SCENARIO: NEWCASTLE, UK
  54. 54. 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. Three different projects were assessed: • District heating projection • Individual fossil fuel solutions • Ground source heat pump DEMONSTRATION SCENARIO: COPENHAGEN, DENMARK
  55. 55. DEMONSTRATION SCENARIO: TORINO, ITALY
  56. 56. 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.
  57. 57. www.eecities.com
  58. 58. www.semanco-tools.eu
  59. 59. A platform which enables expert users to create energy models of urban areas to assess the current peformance of buildings and to develop 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:
  60. 60. URSOS Energy calculation engine GIS data Census CadastreClimate Typology Socio-Economic Energy-related data Semantic Energy Information Framework Integrated Platform ELITE Federation engine Ontology OWL-DL liteA URSOS Input form3D Maps 1 2 3 5 4 ONTOLOGY DESIGN TOOLS
  61. 61. www.semanco-tools.eu ONTOLOGY DESIGN TOOLS: Click-On ©Faculty of Business and Computer Science, Hochschule Albstadt-Sigmaringen, GERMANY
  62. 62. www.semanco-tools.eu ONTOLOGY DESIGN TOOLS: Map-On ©ARC Engineering and Architecture La Salle, SPAIN
  63. 63. ONTOLOGY DESIGN TOOLS: Map-On
  64. 64. Relational database Domain ontology AutoMap4OBDA R2RML mappings that can be modified and improved in Map-On ONTOLOGY DESIGN TOOLS: AutoMap4OBDA
  65. 65. 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
  66. 66. 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.
  67. 67. The OPTIMUS DSS will be tested in three municipalities across Europe: • Savona, ITALY • Sant Cugat del Vallès, SPAIN • Zaanstad, THE NETHERLANDS
  68. 68. 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 prediction models are implemented in the DSS engine Historical data Predicted data Monitored data Relations between input data (real time and predicted data, and static user inputs) for suggesting an action plan ACTION PLANS
  69. 69. OPTIMUS DSS City dashboard
  70. 70. OPTIMUS DSS Building dashboard
  71. 71. OPTIMUS DSS Monitored data
  72. 72. Optimization of the boost time of the heating/cooling system
  73. 73. Optimization of selling/consumption of electricity produced by a PV system
  74. 74. Triple store (Virtuoso Server) Semantic Framework Weather data De-centralized data Feedback occupants Ztreamy Server Energy prices Energy production 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
  75. 75. Data source Publisher Ztreamy server Semantic service Virtuoso triple store WP2 Data capturing modules WP3 Semantic Framework WP3 Optimus DSS Subscriber Data capturing module T3.2, T3.3 DSS Engine T3.4 DSS interfaces SEMANTIC INTEGRATION PROCESS
  76. 76. Data source Publisher Ztreamy server Semantic service Virtuoso triple store WP2 Data capturing modules WP3 Semantic Framework WP3 Optimus DSS Subscriber Data capturing module T3.2, T3.3 DSS Engine T3.4 DSS interfaces RDF template for data capturing modules RDF DATA Raw data SEMANTIC INTEGRATION PROCESS
  77. 77. RDF data from modules + context data + + + Data source Publisher Ztreamy server Semantic service Virtuoso triple store WP2 Data capturing modules WP3 Semantic Framework WP3 Optimus DSS Subscriber Data capturing module T3.2, T3.3 DSS Engine T3.4 DSS interfaces RDF DATA RDF DATA + CONTEXT Raw data SEMANTIC INTEGRATION PROCESS
  78. 78. 2. Implement integration methods based on pub/sub systems (e.g. Ztreamy) Data source RDF data from modules + contextual data + + + Data source Publisher Ztreamy server Semantic service Virtuoso triple store WP2 Data capturing modules WP3 Semantic Framework WP3 Optimus DSS Subscriber Data capturing module T3.2, T3.3 DSS Engine T3.4 DSS interfaces
  79. 79. 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)
  80. 80. 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:Deployment ssn: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 subClassOf ssn: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
  81. 81. 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:
  82. 82. • 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:
  83. 83. 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
  84. 84. 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
  85. 85. OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design SWIMing VoCamp Workshop | Dublin, 22–23 March 2016 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
  86. 86. OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design SWIMing VoCamp Workshop | Dublin, 22–23 March 2016 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 DPIs calculation and Scenario optimization Insert BASELINE Estimation of the performance * DPI = District Performance Indicator
  87. 87. OptEEmAL GA no. 680676 | A comprehensive ontologies-based framework to support retrofitting design SWIMing VoCamp Workshop | Dublin, 22–23 March 2016 Simulation Data models District Data Model – Scenario Generation 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 Energy Conservation Measures (ECMs) catalogue Select DPIs calculation and Scenario optimization Insert
  88. 88. ©ARC Engineering and Architecture La Salle www.salleurl.edu/arc arc@salleurl.edu

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