The document discusses the SEMANCO project, which aimed to create a platform using semantic technologies to enable experts from different domains to develop and deploy urban energy models. These models help stakeholders understand carbon reduction in urban areas. The project integrated energy-related data from multiple sources according to an ontology. It developed use cases, activities and three case studies to test the semantic urban energy models and the integrated SEMANCO platform. The platform includes tools for data visualization, simulation and evaluation of urban energy plans and projects.

1. SEMANCO: semantic data integration
and urban energy models
Álvaro Sicilia
ARC Engineering and Architecture La Salle
Barcelona, SPAIN

2. SEMANCO is a research project is co-funded by the Seventh
Framework Programme on “ICT systems for Energy Efficiency”
from the European Union (2011-15).
The goal was to create platform – i.e. methods and tools –
using semantic technologies which enable experts from
different domains to devise and deploy urban energy
models that help various stakeholders –planners, consultants,
policy makers– to understand the complexity underlying
carbon reduction in urban areas.
OBJECTIVES

3. PROJECT APROACH
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
Smart City Expo World Congress, Barcelona, 18-20 November 2014

4. 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)
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).
A model of an urban energy system fulfils two main purposes:
- to understand the current state of the system
- to help to take decisions to influence its future evolution
URBAN ENERGY SYSTEMS AND MODELS

5. URBAN ENERGY SYSTEMS AND MODELS
An urban energy model provides answers to questions:
• How much energy is consumed in an urban area?
• What is that energy used for?
• What are the energy savings if I carry out a renovation?
Urban energy models relies on data to answer those questions,
but Energy related data is:
- dispersed in numerous proprietary databases / open data
sources and it might have different levels of quality
- heterogeneous since it is generated by different applications in
various domains
- dynamic since urban energy systems are dynamic entities in
continuous transformation

6. THE ROLE OF THE SEMANTIC WEB TECHNOLOGIES
Semantic Web 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 conceptualised by experts.

7. USE CASE METHODOLOGY
A USE CASE is used to capture the
knowledge from various domain experts
A USE CASE delimits a research
problem so it can be handled.
It incorporates a set of
components (actors, data,
requirements, policies, questions
to the model…) and their
interrelationships.
DATA
TOOLS
USERS
Services
Regulations
Stakeholders
USE CASE
Standards
Use case methodology
developed within the project
Based on Neon Methodology
(Suárez-Figueroa et al., 2012)

8. USE CASE 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 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
template

9. USE CASE METHODOLOGY
Acronym A9
Goal Determination of characteristics of urban environment
Urban Scale Meso –Macro (urban area)
Process scale Operational
Actors  The municipality (councilors of urban planning, housing, environment and
countryside, …) (stakeholder)
 Urban Planners, from public authorities or from private companies
 Public company of social housing
 Owner/promoter of the building
 Neighbors association (stakeholder)
Related national/local
policy framework
 National energy code and national technical building construction code (CTE, and
RITE)
 Nation , regional and local urban planning regulations
Issues to be addressed  Volumetric information of the buildings conforming the urban area (to obtain profile
of shadows)
 Geography of the Area
 Location and volume of other urban elements- Climatic information (Horizontal
radiation, wind speed, relative humidity, external temperature)
Input Data
Name Description Domain Format
Vector Maps from
Manresa GIS
Polygon map showing 3D geometry (buildings footprint,
perimeter and height) of the buildings of the urban area
Geography,
Manresa GIS
SHP
GIS maps with
topographic information
Topographic information of the urban area and
surroundings
Geography,
Manresa GIS
SHP
Horizontal radiation Amount of W·h/m2 Climatic
Wind speed Speed of the wind in m/s at the nearest weather station Climatic
Relative humidity Relative humidity at the nearest weather station Climatic
Air temperature Outside Temperature at the nearest weather station Climatic
ACTIVITY
template

10. PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Urban Energy Models are
created for each case study
based on the Uses Cases
and Activities
Collaborative task between
municipality, architects, energy
experts, TIC experts… to define
Use Cases and Activities

11. PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
The SEMANCO platform implements the Urban Energy
Models (to answer Use Cases) using the Semantic Energy
Information Framework (SEIF) to access distributed data

12. PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Standard
Tables
Data sources
mapping Table
Ontology
Mapping tools
Semantic
Energy model
Data sources
integrated
Ontology
Editor
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Use case methodology Platform development
Semantic data
integration process
Ontology building
process
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Defining informally the terms
needed for the ontology
Knowledge formalisation process

13. PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Standard
Tables
Data sources
mapping Table
Ontology
Mapping tools
Semantic
Energy model
Data sources
integrated
Ontology
Editor
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Use case methodology Platform developmentSemantic integration processOntology building process
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
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 (Corrado et al., 2015)

14. PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Standard
Tables
Data sources
mapping Table
Ontology
Mapping tools
Semantic
Energy model
Data sources
integrated
Ontology
Editor
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Use case methodology Platform developmentSemantic integration processOntology building process
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Formalizing the knowledge
by coding the ontology
Knowledge formalisation process

15. Knowledge formalisation process
PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Standard
Tables
Data sources
mapping Table
Ontology
Mapping tools
Semantic
Energy model
Data sources
integrated
Ontology
Editor
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Use case methodology Platform developmentSemantic integration processOntology building process
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Codification of the Standard
Tables into an Ontology in a
computer-processable format
(i.e. OWL).
We have created an ontology
editor which hide the
complexity of ontology editing
process.

16. Knowledge formalisation process
PLATFORM DEVELOPMENT METHODOLOGY
Use Cases &
Activities
Standard
Tables
Data sources
mapping Table
Ontology
Mapping tools
Semantic
Energy model
Data sources
integrated
Ontology
Editor
SEIF
Case Study:
Newcastle
Case Study:
Manresa
Case Study:
Copenhagen
Urban Energy
Model (Data,
Tools, Users)
Use case methodology Platform developmentSemantic integration processOntology building process
Integrated Platform
Urban Energy
Model (Data,
Tools, Users)
Urban Energy
Model (Data,
Tools, Users)
Integration of energy-related
data according to the ontology

17. SEMANCO PLATFORM
Relational
databases
SEMANCO
ontology
Integration of energy-related
data according to the ontology
Map-On: Ontology Mapping editor to
hide the complexity for mapping creation
R2RML
mapping file
This file is used to
transform data
sources into RDF
according to the
SEMANCO ontology

18. SEMANCO PLATFORM
CLUSTER VIEWTABLE VIEW
PERFORMANCE INDICATORS FILTERINGMULTIPLE SCALE VISUALIZATION
Main characteristics of the SEMANCO platform GUI

19. SEMANCO PLATFORM
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

20. SEMANCO PLATFORM
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.
Year of construction
from the Cadastre
Geometry obtained from the 3D model
Street address name and
Street view from Google
Geolocation services
Wall, ground and roof
properties from the building
typologies database
Ventilation from the building
typologies database

21. SEMANCO PLATFORM
Results of the energy simulation carried out by URSOS

22. SEMANCO PLATFORM
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

23. APPLICATION CASES
Torino, ItalyCopenhagen, Denmark
Newcastle, UKManresa, Spain

24. EECITIES OUTCOMES
www.eecities.com

25. PROYECT OUTCOMES
www.semanco-tools.eu

26. References
Keirstead, J., Jennings, M., and Sivakumar, A. (2012). A review of urban energy system models:
approaches, challenges and opportunities. Renewable and Sustainable Energy Reviews, 16(6), pp.
3847-3866.
Keirstead, J. & Shah, N. (2013). Urban energy systems: an integrated approach. Routledge
Suárez-Figueroa, M.C., Gómez-Pérez, A., Motta, E. & Gangemi, A. (2012). Ontology engineering in a
networked world. Springer Science & Business Media
Corrado, V., Ballarini, I., Madrazo, L., & Nemirovskij, G. (2015). Data structuring for the ontological
modelling of urban energy systems: The experience of the SEMANCO project. Sustainable Cities
and Society, 14, 223-235.
Nemirovskij, G., Nolle, A., Sicilia, A., Ballarini, I., Corrado V. (2013). Data Integration Driven Ontology
Design, Case Study Smart City. In Proceedings of the 3rd International Conference on Web
Intelligence, Mining and Semantics (WIMS '13), Madrid (Spain), 12-14 June 2013.
Wolters, M., Nemirovski, G., & Nolle, A. (2013). ClickOn A: An Editor for DL-Lite A Based Ontology
Design. In Description Logics (pp. 1000-1010).
Sicilia, Á., Nemirovski, G., & Nolle, A. (2017). Map-On: A web-based editor for visual ontology
mapping. Semantic Web, 8(6), 969-980.
Nolle, A., & Nemirovski, G. (2013). ELITE: An Entailment-Based Federated Query Engine for Complete
and Transparent Semantic Data Integration. In Description Logics (pp. 854-867).

27. Acknowledgements
SEMANCO has been carried out with the support of the FP7 Program “ICT
systems for Energy Efficiency” of the European Union with the grant number
287534.
ARC Engineering and Architecture La Salle, Spain: Developer of the
SEMANCO integrated platform, ontology design and data integration
University of Teesside, United Kingdom: Developer of the energy assessment
and improvement tools SAPMAP
CIMNE-BeeGroup, Spain: Developer of the USiT tool, multicriteria evaluation
Politecnico di Torino, Italy: Energy data structuring
Hochschule Albstadt-Sigmaringen, Germany: Ontology design, energy data
analytics
Agency 9, Sweden: 3d visualization, developers of 3dMaps
Ramboll, Denmark: Developer of the UEP/UIP evaluation tool
National Energy Action, UK: Building energy experts
Fòrum, Spain: Building energy experts

28. https://timepac2019.blogspot.com
If you would like to have more information
about this presentation, please contact
alvaro.sicilia@salle.url.edu