The Semanco project aims to develop methods and tools for semantic modeling of energy data to aid urban planning stakeholders in reducing CO2 emissions in cities. The project involves creating formalized models and an ontology of energy-related data, enabling the integration and analysis of diverse energy data sources. It has resulted in a comprehensive framework that connects use cases, standards, and data sources to support informed decision-making in urban planning.

1. Modelling Energy Data in Urban Environments
Álvaro Sicilia
ARC Enginyeria i Arquitectura La Salle
Universitat Ramon Llull, Barcelona
MULTIPLE REPRESENTATIONS Barcelona, 11-12 April 2013

2. CONTENTS
The objective of SEMANCO is to provide methods and tools, based on semantic
modelling of energy information, to help different stakeholders involved in urban
planning to make informed decisions about how to reduce CO2 emissions in cities
by:
• 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

3. 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 tools
Technological SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF)
Platform
Building Energy Environmental Economic
repositories data data data

4. ENERGY DATA MODELLING
Energy data modelling as a process of conceptualization, formalization and codification
1. Informal – dispersed 2. Informal - integrated 3. Formal-computable
Dispersed information which Integrated information which Integrated information
can be processed only by can be processed only by formalized to be processed
humans. humans. by humans and computers

5. ENERGY DATA MODELLING
Energy data modelling as a process of conceptualization, formalization and codification
1. Informal – dispersed 2. Informal - integrated 3. Formal-computable
Dispersed information which Integrated information which Integrated information
can be processed only by can be processed only by formalized to be processed
humans. 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

6. 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, …)

7. 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 UC9
A USE CASE Work process
Users
Planning
 Municipal technical planners
specification 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 intervention
Use cases and ACTIVITIES
are connected creating a
tree

8. STANDARD TABLES
Standard 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 -

9. DATA SOURCES MAPPING TABLES
Data source mapping tables maps the data sources (e.g. Database) structure (table and
columns) 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

10. ONTOLOGY
Codification 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

11. ONTOLOGY EDITOR

12. DATA SOURCES INTEGRATION
Codification 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

13. DATA SOURCES INTEGRATION
Ontology Mapping Collaborative Web Environment

14. TWO EXAMPLES
Get U value of a wall of building typologies:
Get U value of a roof of building typologies

15. 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.

16. 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 .