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Smart Cities and Open Data

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Keynote from Leandro Madrazo on "Smart Cities and Open Data" in the 1st Summer School on Smart Cities and Linked Open Data (LD4SC 2015).

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Smart Cities and Open Data

  1. 1. SMART CITIES AND OPEN DATA Dr. Leandro Madrazo Head Research Group ARC Engineering and Architecture La Salle Ramon Llull University, Barcelona, Spain www.salleurl.edu/arc 1st Summer School on Smart Cities and Linked Open Data - Madrid, 7-12 June 2015
  2. 2. 1. Introduction group ARC: Research on energy information systems 2. Smart cities 3. Energy efficient cities: the SEMANCO project
  3. 3. 1. Introduction group ARC: Research on energy information systems 2. Smart cities 3. Energy efficient cities: the SEMANCO project
  4. 4. www.salleurl.edu/arc
  5. 5. ARC – Architecture, Representation, Computation – is an interdisciplinary research group based in the School of Architecture La Salle, Ramon Llull University, Barcelona. It was founded in 1999, since then it has been carrying out research in the application of ICT to architecture www.salleurl.edu/arc
  6. 6. 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: development of energy information systems in buildings and urban environments. •Technology-enhanced learning: collaborative learning environments and digital libraries. •Information spaces: interactive interface design, information visualization, concept maps and data mining. www.salleurl.edu/arc
  7. 7. 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 models and systems:
  8. 8. 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 models and systems:
  9. 9. 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
  10. 10. The purpose of the project was to create building models which would encompass the energy related data created during the overall design process, from design to operation. This way the simulated energy performance of the building could be taken into account in the design processes, and the actual performance could be compared to the simulated one.
  11. 11. 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
  12. 12. 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 RESOURCES 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
  13. 13. 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.
  14. 14. 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.
  15. 15. An operative Energy Information Integration Platform linking the building energy data through the stages of the lifecycle: • Enriching BIM models with energy attributes • Creating three ontologies for building, simulation and performance data (BIM, SIM and PIM ontologies) • Integrating monitoring data (via OPC server) in the EIIP What was achieved in IntUBE:
  16. 16. 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
  17. 17. The aim of this research project has been to design and implement a prototype of a building energy information system using semantic technologies, following the philosophy of the Linked Open Data initiative.
  18. 18. 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
  19. 19. Building ontologies: A process to transfer knowledge from domain experts to ontology engineers- informal method, based on standards Process
  20. 20. 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 Energy model (REPENER Ontology) AEMET ontology Linked GeoData ontology aemet:Temperature Literal : Decimal Excerpts of local ontologies developed in OWL language.
  21. 21. 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)
  22. 22. www.seis-system.org
  23. 23. www.seis-system.org
  24. 24. www.seis-system.org
  25. 25. www.seis-system.org
  26. 26. Integration of data from multiple sources using Semantic Web technologies to create a building energy model • A global ontology representing a building energy model • On-line application focused on specific user profiles What was achieved in RÉPENER:
  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 is to provide semantic tools to different stakeholders involved in urban planning (architects, engineers, building managers, local administrators, citizens and policy makers) to help them make informed decisions about how to reduced carbon emissions in cities.
  29. 29. 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
  30. 30. Data connected through the Semantic Energy Information Framework OPEN SEMANTIC DATA MODELS DATA TOOLS
  31. 31. SEMANCO integrated platform Case studies: Newcastle (UK), Copenhagen (Denmark), Manresa (Spain), Torino (Italy)
  32. 32. 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 • An operative platform which can be implemented in other cities What was achieved in SEMANCO:
  33. 33. 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
  34. 34. The purpose of OPTIMUS is to develop a semantic- based decision support system which integrates dynamic data from five different types / sources: climate, building operation, energy production, energy prices, user’s feedback.
  35. 35. OPTIMUS Urban scale Weather forecast Operation data Social media Energy Prices Renewable energy production SCEAF Data From/for: Buildings  Urban areas Source: Monitored  Calculated Openness: Proprietary Open data Problem Reduction of energy consumption and CO2 emissions of a city by means of optimising the public buildings. The SCEAF measures the impact at a urban scale. DSS The Optimus DSS is designed for supporting decision of particular problems at building scale. An intermediate layer between SCEAF and DSS is needed to have a top-down view of how the actions at building level affects the urban scale DSS Actions with impact at city and building scale Building scale Smart City Energy Assessment Framework
  36. 36. Semantic framework Data mining Metadata (patterns, clusters,…) Rules Inference Engine Front-end environment Admin interface WP3 DSS Weather forecast Energy consumption Social media Energy prices Available RES WP2 Data capturing modules Logic structure of the DSS & Tasks relations
  37. 37. OPTIMUS ontology: - Static data (Building and systems features) can be modelled by extending SEMANCO ontology (http://semanco-tools.eu/ontology- releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl) - Dynamic data (sensoring) can be modelled by extending 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) Step-forward with respect the SEMANCO work: including monitoring data
  38. 38. FRONT-END interface. It suggests when to buy/sell energy produced by PV panels based on weather conditions, energy prices, energy consumption of the building.
  39. 39. The SEMANCO ontology is being 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 will be implemented in three cities (Zaanstad, Savona, Sant Cugat) What is being done in OPTIMUS:
  40. 40. 1. Introduction group ARC: Research on energy information systems 2. Smart cities 3. Energy efficient cities: the SEMANCO project
  41. 41. Cities are complex systems made up of physical elements – buildings and streets, energy supply and communication infrastructures – in which multiple actors –citizens, companies, organizations– interact to carry out activities which put into relation the multiple subsystems –economic development with transportation networks, energy consumption with buildings energy performance – which make the city. “Cities in fact are a ‘mess’ [a system of problems] as defined by organisational theorist and management scientist Russell Ackoff a complex system of systems where each problem interacts with others and there are no clear solutions” [M. Khawaja, 2014, Are smart cities really that smart?] SMART CITIES
  42. 42. The term smart is used in everyday speech to refer to ideas and people that provide clever insights [M.Batty et al, 2012, Smart Cities of the Future] Smart refers also to a capacity to quickly adapt to a changing environment, in the biological sense (e.g. smart growth) SMART CITIES
  43. 43. Wired cities, intelligent cities, virtual cities, digital cities, information cities … “Smart cities are often pictured as constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials in terms of the flow of decisions about the physical and social form of the city.” [M. Batty et al. , 2012, Smart cities of the future] SMART CITIES
  44. 44. ICT might improve the functioning of cities, enhancing their efficiency, improving their competitiveness, and providing new ways in which problems of poverty, social deprivation, and poor environment might be addressed “The new intelligence of cities, then, resides in the increasingly effective combination of digital telecommunication networks (the nerves), ubiquitously embedded intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence)” [T. Nam & T. A. Pardo, 2011, Conceptualizing Smart City with Dimensions of Technology, People, and Institutions] SMART CITIES
  45. 45. Where does the intelligence lie? • In the data (ontologies)? • In the processes/functions to analyze the data? • In the people who interpret the analyses? • In the city as a whole (in its infrastructure, networks, people)? • In the overall system of the city or in each of the city’s subsystem? SMART CITIES
  46. 46. “A smarter city infuses information into its physical infrastructure to improve conveniences, facilitate mobility, add efficiencies, conserve energy, improve the quality of air and water, identify problems and fix them quickly, recover rapidly from disasters, collect data to make better decisions, deploy resources effectively, and share data to enable collaboration across entities and domains…..” [T. Nam & T. A. Pardo, 2011, Conceptualizing Smart City with Dimensions of Technology, People, and Institutions] SMART CITIES
  47. 47. “…….However, infusing intelligence into each subsystem of a city, one by one–– transportation, energy, education, health care, buildings, physical infrastructure, food, water, public safety, etc.—is not enough to become a smarter city. A smarter city should be treated as an organic whole––as a network, as a linked system [T. Nam & T. A. Pardo, 2011, Conceptualizing Smart City with Dimensions of Technology, People, and Institutions] SMART CITIES
  48. 48. “We believe a city to be smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory governance.” [A. Caragliu, C. del Bo, P. Nijkamp, 2009, Smart cities in Europe] SMART CITIES
  49. 49. Massive streams of data (big data) are being produced every data (transport, energy ….) captured by sensors, mobile devices,… It is assumed that by getting real time information about the city’s subsystems we can know how the city functions, and take actions to improve its functioning. This implies: ₋ getting the data (accurate, maintained, reliable) ₋ integrating data from multiples sources, types (static, dynamic) and forms ₋ extracting meanings from the data SMART CITIES : DATA
  50. 50. SMART CITIES : DATA : MODELS Deriving insights and theories from continuous streaming of data (data mining/reality mining): patterns, routines, models….. Do we need models to understand how the smart city works? Is it enough to identify correlations between phenomena without asking for the cause? Is data derived from reality? Or is reality constructed after the data?
  51. 51. SMART CITIES : CHALLENGES •Challenges are not only technological; cities are not only data • So far urban planning has been based on long-term visions, confined to certain scales (regional, municipal, …) •Now new forms of planning are needed based on the short-term rather than in long-term, more interdisciplinary and participative, overcoming spatial limits and institutional boundaries. • More participative leadership, making citizens actors of the development of the city, contributing to innovation
  52. 52. SMART CITIES : CHALLENGES “Leading a smart city initiative requires a comprehensive understanding of the complexities and interconnections among social and technical factors of services and physical environments in a city. For future research based on a socio-technical view, we must explore both ‘how do smart technologies change a city?’ and ‘how do traditional institutional and human factors in urban dynamics impact a smart city initiative leveraged by new technologies?’” [T. Nam & T. A. Pardo, 2011, Conceptualizing Smart City with Dimensions of Technology, People, and Institutions]
  53. 53. SMART CITIES : BUT………… “Every technology and every ensemble of technologies encodes a hypothesis about human behaviour, and the smart city is not different” [A. Greenfield, 2013, Against the smart city]
  54. 54. SMART CITIES : BUT………… “The underlying logic of computational decision-making at city level is based on a rationalistic assumption that data is impartial and it gives us facts, which leads to truth, and then wisdom, understanding and control. If data actually is impartial, then decisions based on it should be superior in every context. It is the absolutism of data that is so attractive to decision makers, because it absolves them of any moral responsibility. Sanitised data eliminates room for doubt and argument. Data being binary eradicates ethical dilemmas and obviates the need for agency, accountability and creativity.” [M. Khawaja, 2014, Are smart cities really that smart?]
  55. 55. 1. Introduction group ARC: Research on energy information systems 2. Smart cities 3. Energy efficient cities: the SEMANCO project
  56. 56. SEMANCO ‘s comprehensive approach: 1. Modelling energy efficiency problems with experts 2. Structuring energy related data 3. Creating an ontology of the urban energy performance domain 4. Creating an integrated platform: • Integrating data and tools in a platform • Visualizing information • Analyzing data
  57. 57. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4 Getting heterogeneous, distributed energy related data
  58. 58. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4 Getting heterogeneous, distributed energy related data Modelling data with ontologies
  59. 59. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4 Getting heterogeneous, distributed energy related data Modelling data with ontologies Providing tools and services to interoperate with data
  60. 60. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4 Getting heterogeneous, distributed energy related data Modelling data with ontologies Providing tools and services to interoperate with data Using tools at different decision making realms
  61. 61. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4 Getting heterogeneous, distributed energy related data Modelling data with ontologies Providing tools and services to interoperate with data Using tools at different decision making realms Reducing carbon emissions
  62. 62. 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 WP2 WP6 WP8 Technological Platform SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) CO2 emissions reduction! Application domains Stakeholders WP3 WP5 WP4
  63. 63. 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).
  64. 64. 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 help different stakeholders –planners, politicians, citizens – to assess the energy performance at the different urban scales –building, district, neighborhood– and to take decisions which help to improve it.
  65. 65. A model of an urban energy system fulfils two main purposes (Shah, 2013): - to understand the current state of the system - to help to take decisions to influence its future evolution An urban energy model provides answers to questions (e.g. how much energy is consumed in an urban area, what is that energy used for, what are the connections between urban density and energy demand).
  66. 66. Models of urban systems rely on data: the data which is necessary to reproduce the city’s physical structure (e.g. GIS data) ; the data generated by the activity of people, goods, and services. Energy related data is dispersed in numerous databases and open data sources and it might have different levels of quality; it is heterogeneous since it is generated by different applications in various domains; and it is dynamic, since urban energy systems are dynamic entities in continuous transformation.
  67. 67. Semantic technologies are useful to integrate data from multiple domains and applications. 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.
  68. 68. Integrated Platform Data sources (Distributed and heterogeneous) External Embedded Interfaced SEIF Semantic Energy Model (global ontology) URBAN ENERGY MODELS Data ToolsUsers Tools Private Open LOD Applications
  69. 69. Data connected through the Semantic Energy Information Framework DATA TOOLS Smart City Expo World Congress, Barcelona, 18-20 November 2014 INTEROPERABILITY OF DATA AND TOOLS
  70. 70. Data connected through the Semantic Energy Information Framework DATA TOOLS Smart City Expo World Congress, Barcelona, 18-20 November 2014 INTEROPERABILITY OF DATA AND TOOLS
  71. 71. Data connected through the Semantic Energy Information Framework DATA TOOLS Smart City Expo World Congress, Barcelona, 18-20 November 2014 INTEROPERABILITY OF DATA AND TOOLS
  72. 72. 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 multipledeprivation(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 multipledeprivation: 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 - Data: - Tools: - Users: 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 [n] Urban Energy System AN INTEGRATED PLATFORM FOR PLANNING ENERGY EFFICIENT CITIES Integration of multiple data and knowledge in a platform which enables the creation of energy models of an urban energy system Plan A Plan B
  73. 73. 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 multipledeprivation(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 multipledeprivation: 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 - Data: - Tools: - Users: 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 [n] Urban Energy System AN INTEGRATED PLATFORM FOR PLANNING ENERGY EFFICIENT CITIES Integration of multiple data and knowledge in a platform which enables the creation of energy models of an urban energy system Plan A Plan B
  74. 74. Use Cases & Activities Standard Tables Data sources mapping Table Ontology Mapping Semantic Energy model Data sources integrated Ontology Editor 2 4 5 S E I F 6 Case Study: Newcastle Case Study: Manresa Case Study: Copenhagen 1 Use case methodology Semantic integration processOntology building process n A task of the ontology design methodology Relations between outputs of the tasks Output of a task Tool applied in a task to generate its outputs Informal Formal 3 SEMANTIC ENERGY INFORMATION FRAMEWORK
  75. 75. Use Cases & Activities Standard Tables Data sources mapping Table Ontology Mapping Semantic Energy model Data sources integrated Ontology Editor 2 4 5 S E I F 6 Case Study: Newcastle Case Study: Manresa Case Study: Copenhagen 1 Use case methodology Semantic integration processOntology building process n A task of the ontology design methodology Relations between outputs of the tasks Output of a task Tool applied in a task to generate its outputs Informal Formal 3 SEMANTIC ENERGY INFORMATION FRAMEWORK
  76. 76. USE CASE SPECIFICATION DATA TOOLS USERS services stakeholders ENERGY MODEL (formalized as ontologies) USE CASE CASE STUDY regulations A USE CASE is used to capture the knowledge from various domain experts
  77. 77. USE CASE SPECIFICATION USE CASES help to 1. select data sources, 2. identify tool requirements, and 3. define energy model (ontology) Use Case 3 Use Case 2 Use Case 1 Case Study : Manresa Case Study : Copenhagen DATA SOURCES Case Study : Newcastle UC1 A1 A2 A3 A5 A4 ENERGY MODEL (ontology specification) TOOLS A USE CASE is used to capture the knowledge from various domain experts
  78. 78. USE CASE SPECIFICATION Acronym UC10 Goal To calculate the energy consumption, CO2 emissions, costs and /or socio-economic benefits of an urban plan for a new or existing development. Super-use case None Sub-use case UC9 Work process Planning Users  Municipal technical planners  Public companies providing social housing providers  Policy Makers Actors  Neighbour’s association or individual neighbours: this goal is important for them to know the environmental and socio-economic implications of the different possibilities in the district or environment, mainly in refurbishment projects.  Mayor and municipal councillors: In order to evaluate CO2 emissions impact of different local regulations or taxes Related national/local policy framework  Sustainable energy action plan (Covenant of Mayors)  Local urban regulations (PGOUM, PERI, PE in Spain)  Technical code of edification and national energy code (CTE, Calener in Spain) Activities  A1.- Define different alternatives for urban planning and local regulations  A2.- Define systems and occupation (socio-economic) parameters for each alternative  A3. Determine the characteristics of the urban environment  A4. Determine the architectural characteristics of the buildings in the urban plans  A5. Model or measure the energy performance of the neighbourhood  A6. Calculate CO2 emissions and energy savings for each proposed intervention  A7. Calculate investment and maintenance costs for each proposed intervention Use cases and ACTIVITIES are connected creating a tree A USE CASE specification template
  79. 79. USE CASE SPECIFICATION
  80. 80. Use Cases & Activities Standard Tables Data sources mapping Table Ontology Mapping Semantic Energy model Data sources integrated Ontology Editor 2 4 5 S E I F 6 Case Study: Newcastle Case Study: Manresa Case Study: Copenhagen 1 Use case methodology Semantic integration processOntology building process n A task of the ontology design methodology Relations between outputs of the tasks Output of a task Tool applied in a task to generate its outputs Informal Formal 3 SEMANTIC ENERGY INFORMATION FRAMEWORK
  81. 81. Description Reference Type of data Unit Reference to other sheets construction as a whole, including its envelope and all technical building systems, for which energy is used to condition the indoor climate, to provide domestic hot water and illumination and other services related to the use of the building EN 15603 - - - has name (ID) of the building - string - - has construction period of the building - string - - is year of construction of the building - string - - is period of years to be defined according to typical construction or building properties (materials, construction principles, building shape, ...) TABULA string - - first year of the age class TABULA string - - last year of the age class TABULA string - - specification of the region the age class is defined for TABULA string - - - SUMO A,B,C,D - - has use of the building - string - "b_use" has geometry of the building - - - - has number of floors/storeys of the building TABULA* integer - - has usable part of a building that is situated partly or entirely below ground level EN ISO 13370 string - - has number of apartments of the building TABULA integer - - has enclosed space within a building ANSI/ASHRAE 90.1 string - - is heated and/or cooled space EN 15603 EN ISO 13790 ANSI/ASHRAE 90.1 string - - has geometry of the conditioned space of the building - - - "cs_geometry" has the exterior plus semi-exterior portions of a building (separing conditioned space from external environment or from unconditioned space) ANSI/ASHRAE 90.1* - - "cs_envelope" has portions of a building within the conditioned space - - - "cs_internal_partitions" has characteristics of the conditioned space occupancy - - - "cs_occupancy" has arithmetic average of the air temperature and the mean radiant temperature at the centre of a zone or conditioned space EN ISO 13790* - - "cs_indoor_air_temperature" has characteristics of the ventilation of the conditioned space - - - "cs_ventilation" has heat provided within the building by occupants (sensible metabolic heat) and by appliances such as domestic appliances, office equipment, etc., other than energy intentionally provided for heating, cooling or hot water preparation EN ISO 13790 - - "cs_internal_heat_gains" has energy referred to building conditioned space - - - "energy_quantities" Number_Of_Apartments Number_Of_Complete_Storeys Basement CS_Geometry CS_Envelope CS_Internal_Partitions CS_Occupancy CS_Indoor_Air_Temperature CS_Ventilation CS_Internal_Heat_Gains Energy_Quantity_Related_To_Conditioned_Space Building_Use Building_Geometry Space Name/Acronym Building Age Year_Of_Construction Age_Class To_Year has Allocation has has Identifier From_Year Building_Name has Conditioned_Space ENERGY STANDARD TABLES
  82. 82. A total number of 25 Energy Standard Tables were produced, covering different domains (i.e. data categories) and encompassing 987 concepts, which have been included in the ontology. A high quantity of data is accessed through the SEIF, including the data generated by the tools integrated in the SEMANCO platform. ENERGY STANDARD TABLES
  83. 83. Use Cases & Activities Standard Tables Data sources mapping Table Ontology Mapping Semantic Energy model Data sources integrated Ontology Editor 2 4 5 S E I F 6 Case Study: Newcastle Case Study: Manresa Case Study: Copenhagen 1 Use case methodology Semantic integration processOntology building process n A task of the ontology design methodology Relations between outputs of the tasks Output of a task Tool applied in a task to generate its outputs Informal Formal 3 SEMANTIC ENERGY INFORMATION FRAMEWORK
  84. 84. ONTOLOGY DESIGN TOOLS Click-On is an ontology editor developed as a tool for cooperative ontology design, involving ontology designers and domain experts, such as building engineers and energy consultants)
  85. 85. ONTOLOGY DESIGN TOOLS Map-On is a collaborative ontology mapping environment which supports different users –domain experts, data owners, and ontology engineers– to integrate data in a collaborative way using standard semantic technologies
  86. 86. www.semanco-tools.eu
  87. 87. Smart City Expo World Congress, Barcelona, 18-20 November 2014 SEMANCO platform interface displaying the urban model of the Manresa city based on aerial images, terrain model and GIS data. URBAN ENERGY MODELS, PLANS, PROJECTS URBAN, BUILDING PERFORMANCE INDICATORS VISUALIZATION MODES FILTERS INTEGRATED PLATFORM
  88. 88. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Once a baseline reflecting the current state of the urban energy model has been created, different visualiztion tools can be used to identify problem areas. Cluster viewTable view Performance indicators filtering Multiple scale visualization INTEGRATED PLATFORM
  89. 89. Smart City Expo World Congress, Barcelona, 18-20 November 2014 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 PLATFORM FUNCTIONALITIES
  90. 90. Smart City Expo World Congress, Barcelona, 18-20 November 2014 3D model created after the GIS of the Manresa city INTEGRATED PLATFORM : URBAN ENERGY MODEL
  91. 91. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Creation of an Urban Energy Model INTEGRATED PLATFORM : URBAN ENERGY MODEL
  92. 92. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Selection of the tool for creating the baseline in the Urban Energy Model. Each tool includes the regulatory framework, a general description, the methodology and the data sources required by the tool. INTEGRATED PLATFORM : URBAN ENERGY MODEL
  93. 93. Smart City Expo World Congress, Barcelona, 18-20 November 2014 After selecting the tool, the data sources can be personalized by the user INTEGRATED PLATFORM : URBAN ENERGY MODEL
  94. 94. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Finally, the users who are going to participate in the Urban Energy Model are selected. INTEGRATED PLATFORM : URBAN ENERGY MODEL
  95. 95. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Energy performance baseline of an urban area. Energy demand of buildings calculated with an energy assessment tool (URSOS) integrated in the platform. INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  96. 96. Smart City Expo World Congress, Barcelona, 18-20 November 2014 information concerning the selected building which have not yet assessed Building geometry obtained from the 3D model Street address obtained from Google Geolocation services Performance indicators calculated with energy assessment tool Year of construction obtained from the cadastre INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  97. 97. 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. INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  98. 98. 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. 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 INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  99. 99. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Results of the energy simulation carried out by URSOS INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  100. 100. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Creating plans to improve energy efficiency of buildings INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS
  101. 101. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Energy performance baseline of an urban area. Energy demand of buildings calculated with an energy assessment tool (URSOS) integrated in the platform. INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE
  102. 102. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Selecting buildings which belong to the plan at stake. They have been spotted before with the baseline assessment tools. INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS
  103. 103. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Projects to apply improvement measures INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS
  104. 104. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Current status of the buildings before applying measures INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS
  105. 105. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Applying improvements. For example, renovating the existing windows or replacing them with new ones INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS
  106. 106. Smart City Expo World Congress, Barcelona, 18-20 November 2014 Results after applying the improvement measures INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS
  107. 107. Smart City Expo World Congress, Barcelona, 18-20 November 2014 INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS : EVALUATION 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.
  108. 108. INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS : EVALUATION 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.
  109. 109. DEMONSTRATION SCENARIO: MANRESA, SPAIN Purpose: Assessment of the effectiveness of the measures to refurbish buildings in two neighbourhoods. Users: Architect, Industrial Engineer, Engineer, Urban Planner Data sources: Cadastre, census, socio-economic, building typologies (u-values, windows properties, systems…) Tools: URSOS simulation engine Projects: • Building envelope: upgrading windows • Heating system improvement: acquiring new high efficient boilers • Use of renewable energies: installing energy generation systems fed with renewable sources.
  110. 110. DEMONSTRATION SCENARIO: NEWCASTLE, UK Purpose: To identify housing buildings with a high risk of fuel poverty and to propose measure to upgrade them. Users: Energy consultant contracted by Newcastle City Council Data sources: Lower Level Super Output Area (LLSOA): income, fuel poverty, Index of multiple deprivation. Tools: SAP – Simplified Assessment Procedure Projects: • Insulation based refit • Renewables refit • Targeted fabric refit
  111. 111. DEMONSTRATION SCENARIO: COPENHAGEN, DENMARK Purpose: To assess different strategies regarding supply of energy, based both on central and distributed solutions in a greenfield planning situation. Users: Urban planner from the Environmental Department of the Municipality Data sources: building typologies (supply technologies, energy demand), carbon emission coefficients. Tools: Built-in platform tools (UEP, Urban Energy Planning) Projects: • District heating projection • Individual fossil fuel solutions • Ground source heat pump
  112. 112. DEMONSTRATION SCENARIO: TORINO, ITALY Purpose: Assessment of the effectiveness of the measures to refurbish buildings in a neighbourhood of the city. Users: Urban planner from the Environmental Department of the Municipality Data sources: building typologies (supply technologies, energy demand), carbon emission coefficients. Tools: Built-in platform tools (UEP, Urban Energy Planning) Projects: • Low emission windows • Extra wall insulation • Photovoltaic panels
  113. 113. 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.
  114. 114. www.eecities.com
  115. 115. www.semanco-project.eu
  116. 116. If you would like to have more information about this presentation, please contact us: madrazo@salleurl.edu www.salleurl.edu/arc

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