SEMANCO Workshop Theme1 - Semanco
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SEMANCO Workshop: Analysing and Visualising energy related data in our buildings, towns, and cities. ...

SEMANCO Workshop: Analysing and Visualising energy related data in our buildings, towns, and cities.

http://semanco-visualization-workshop.blogspot.com.es/

La Salle Campus Barcelona, Spain, 11-12 April 2013.

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SEMANCO Workshop Theme1 - Semanco SEMANCO Workshop Theme1 - Semanco Presentation Transcript

  • Modelling Energy Data in Urban EnvironmentsÁlvaro SiciliaARC Enginyeria i Arquitectura La SalleUniversitat Ramon Llull, BarcelonaMULTIPLE REPRESENTATIONS Barcelona, 11-12 April 2013
  • CONTENTSThe objective of SEMANCO is to provide methods and tools, based on semanticmodelling of energy information, to help different stakeholders involved in urbanplanning to make informed decisions about how to reduce CO2 emissions in citiesby:• Supporting access to and analysis of distributed and heterogeneous sources of energy related data• Modelling energy data according to standards of the Semantic Web• Providing integrated tools that access and update the semantically modeled data
  • CO2 emissions reduction! Enabling scenarios for stakeholders Application Regulations Planning strategies Urban Developments Building Operations domains Stakeholders Policy Makers Planners Designers/Engineers Building Managers Citizens Building stock Advanced energy Energy simulation Interactive energy modelling information and trade-off tool design tool tool analysis toolsTechnological SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF) Platform Building Energy Environmental Economic repositories data data data
  • ENERGY DATA MODELLING Energy data modelling as a process of conceptualization, formalization and codification1. Informal – dispersed 2. Informal - integrated 3. Formal-computableDispersed information which Integrated information which Integrated informationcan be processed only by can be processed only by formalized to be processedhumans. humans. by humans and computers
  • ENERGY DATA MODELLING Energy data modelling as a process of conceptualization, formalization and codification1. Informal – dispersed 2. Informal - integrated 3. Formal-computableDispersed information which Integrated information which Integrated informationcan be processed only by can be processed only by formalized to be processedhumans. humans. by humans and computers Use cases Standard Tables Ontology Standards & Urban planners references Data sources integration Data sources Mapping Analysis and visualization Data sources Tables Tools/Services
  • INFORMAL – DISPERSED Energy data informally expressed and dispersed in different places and formats Use cases: Run energy performance analysis - To define the energy performance baseline of a City, Neighborhood, Tools requirements and Buildings. - To assess energy impact on new interventions (e.g. building Data sources needed refurbishment, new planning, new policies…) International Standards & References - International technical standards (e.g. EN ISO 13786 , EN 15193 , EN 15251, NREL/TP-550-38600, …) Terminology - Energy data modelling references (e.g. Tabula, Datamine, …) Energy data sources - GIS (e.g. Terrain images, 3D building models, Land registry, …) Terminology - Year of construction-based typologies (e.g. energy consumption, socio- economic, envelop properties, HVAC system, …) Data sources - Climate (e.g. temperature, solar radiance, …)
  • USE CASES METHODOLOGY 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 None case Sub-use case UC9A USE CASE Work process Users Planning  Municipal technical plannersspecification template  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  Sustainable energy action plan (Covenant of Mayors) national/local  Local urban regulations (PGOUM, PERI, PE in Spain) policy  Technical code of edification and national energy code (CTE, Calener in Spain) framework 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 interventionUse cases and ACTIVITIESare connected creating atree
  • STANDARD TABLESStandard tables collect and classifies the information and knowledge from different sources:Use cases, Standards and data. - 24 categories including building use, climate, territory, socio-economic, and building geometry. - Each category contains terms and their relations (aggregation, subsumption) - Each term is referred to a specific Standard (EN 15603, TABULA,…), is typed (String, integer,…), and if it is applicable is measured (square meters, CO2 tons per year…) Name/Acronym Description Reference Type of data Unit construction as a whole, including its envelope and all technical building systems, for which energy is used to Building condition the indoor climate, to provide EN 15603 - - domestic hot water and illumination and other services related to the use of the building has Building_Name name (ID) of the building - string - has Age construction period of the building - string - is Year_Of_Construction year of construction of the building - string - period of years to be defined according to typical construction or building properties is Age_Class TABULA string - (materials, construction principles, building shape, ...) has From_Year first year of the age class TABULA string - has To_Year last year of the age class TABULA string - specification of the region the age class is has Allocation TABULA string - defined for has Identifier - SUMO A,B,C,D - has Address address of the building - string - first part of the postcode of the building has First_Part_Of_Postcode SAP string - location has Building_Typology building typology - string - is Flat apartment in a building - string - is Detached_Building small building, without attached buildings TABULA string - is Semi-Detached_Building small building, with an attached building TABULA string -
  • DATA SOURCES MAPPING TABLESData source mapping tables maps the data sources (e.g. Database) structure (table andcolumns) to the Standard Tables previously developed Data source Data name (in the Data Data name (according to Data category source) standard tables) Tb_PercentageWindowArea-AgeConstruction Percentage_Windows_Area Percentage_Windows_Area Not classified data Tb_WindowParameters-YearConstruction Window_U-value Window_U-value Building technical data Tb_WindowParameters-YearConstruction Window_Glass_g-value Window_Glass_g-value Building technical data Tb_RoofUValue-YearConstruction Roof_U-value Roof_U-value Building technical data Tb_SkylightParameters-YearConstruction Skylight_U-Value Skylight_U-Value Building technical data Tb_SkylightParameters-YearConstruction Skylight_Glass_g-value Skylight_Glass_g-value Building technical data Tb_Manresa_Climate Global_Solar_Irradiance Global_Solar_Irradiance Climatic data Tb_Manresa_Climate Air_Temperature_Maximum Air_Temperature_Maximum Not classified data Tb_Manresa_Climate Air_Temperature_Minimum Air_Temperature_Minimum Not classified data … … … … INFORMAL SHARED VOCABULARY
  • ONTOLOGYCodification of the Standard Tables into an Ontology - It is coded in OWL language (it can be seen as a XML file which can be processed by computers) - An Ontology is composed of two types of hierarchies: a) Subsumption (Taxonomy) b) Aggregation (Properties) - We have created an ontology editor which hide the complexity of ontology editing process. - 868 Concepts, 405 relations, and 278 properties a) Subsumption hierarchy b) Aggregation hierarchy FORMAL SHARED VOCABULARY
  • ONTOLOGY EDITOR
  • DATA SOURCES INTEGRATIONCodification of the Standard Tables into an Ontology SPARQL Urban planners SQL-SPARQL Data source Rewritter RDF Analysis and visualization Tools/Services Ontology Mapping Collaborative Web Environment
  • DATA SOURCES INTEGRATIONOntology Mapping Collaborative Web Environment
  • TWO EXAMPLESGet U value of a wall of building typologies: Get U value of a roof of building typologies
  • CONCLUSIONS SUMMARY• We have implemented a set of procedures, templates, methods, tools to conceptualize energy data in urban planning.• The energy related data –use cases, standards, data sources– have been represented in different ways from informally to formal format enabling their processing by computers.• An ontology including more than 800 concepts has been created modelling the energy- related data in the urban planning domain.• This way, different data sources from different domains could be integrated and could be accessed using the same terminology.
  • CONCLUSIONS If you would like more information, please contact us sicilia@salleurl.edu or visit our web site www.semanco-project.eu SEMANCO is being carried out with the support of the European Union’s FP7 Programme “ICT for Energy Systems” 2011-2014, under the grant agreement number 287534 .