Smart Cities and Open Data

SMART CITIES AND OPEN DATA

Dr. Leandro MadrazoHead Research GroupARC Engineering and Architecture La SalleRamon Llull University, Barcelona, Spainwww.salleurl.edu/arc

1st Summer School on Smart Cities and Linked Open Data - Madrid, 7-12 June 2015

1. Introduction group ARC: Research on energy information systems

2. Smart cities

3. Energy efficient cities: the SEMANCO project


2. Smart cities


www.salleurl.edu/arc

ARC – Architecture, Representation, Computation – is aninterdisciplinary 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 outresearch in the application of ICT to architecture


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.


2008-2011 IntUBE: Intelligent use of building’s energy information7th 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 Planning7th Framework Programme / Coordinator: ARC Engineering and Architecture La Salle, Spain

2013-2016 OPTIMUS: Optimising the energy use in cities with smart decision support system7th 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:

2008-2011 IntUBE: Intelligent use of building’s energy information7th 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 Planning7th Framework Programme / Coordinator: ARC Engineering and Architecture La Salle, Spain

2013-2016 OPTIMUS: Optimising the energy use in cities with smart decision support system7th 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:

IntUBE Intelligent use of building’s energy information2008-2011 / 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

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.

EIIP – Energy Information Integration Platform

BIM server SIM server RD serverPIM server

Co

nc

ep

t

De

sig

n d

ev

elo

p.

Simulation tool

Building lifecycle

Co

ntr

ol /

ma

inte

na

nc

e

Re

tro

fit

de

sig

n

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

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

Performanceindicators

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

S10S10S1S1

S5S5

S17S17 S15S15 S13S13S14S14

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.

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.

An operative Energy Information Integration Platformlinking 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:

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 LlullUniversity (Project Coordinator), SPAIN

• Faculty of Business and Computer Science, HochschuleAlbstadt-Sigmaringen, GERMANY

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.

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

Building ontologies: A process to transfer knowledge from domain experts to ontology engineers- informal method, based on standards

Process

Certificate

BuildingDomain

icaen:certificates

ProjectData Literal : Stringicaen:ID_LOCALITAT

icaen:hasProject

WeatherStation

Point

rdfs:labelaemet:stationName

Literal : String Literal : String

geo:Location

geo:lat

geo:long

Literal : DecimalLiteral : Decimal

Town

geo:latgeo: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 GeoDataontology

aemet:Temperature

Literal : Decimal

Excerpts of local ontologies developed in OWL language.

Certificate

BuildingDomain

icaen:certificates

ProjectData Literal : Stringicaen:ID_LOCALITAT

icaen:hasProject

WeatherStation

Point

rdfs:labelaemet:stationName

Literal : String Literal : String

geo:Location

geo:lat

geo:long

Literal : DecimalLiteral : Decimal

Town

geo:latgeo: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 GeoDataontology

Located

Mappings between ontologies are created to interrelate data sources allowing integrated queries.

Knowledge discovery process (we use tools like SILK for finding relationships)

www.seis-system.org

www.seis-system.org

www.seis-system.org

www.seis-system.org

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:

SEMANCO Semantic Tools for Carbon Reduction in Urban Planning2011-2014 / 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

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.

Building repositories

Energydata

Environmentaldata

Economicdata

Enabling scenarios for stakeholders

Building stock energy modelling

tool

Advanced energy information

analysis tools

Interactivedesign tool

Energy simulationand trade-off tool

Policy Makers CitizensDesigners/Engineers Building ManagersPlanners

Regulations Urban Developments Building OperationsPlanning strategies

TechnologicalPlatform

SEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF)

CO2 emissions reduction!

Application domains

Stakeholders

Data connected through the Semantic Energy Information Framework

OPEN SEMANTIC DATA MODELS

DATA TOOLS

SEMANCO integrated platform

Case studies: Newcastle (UK), Copenhagen (Denmark), Manresa (Spain), Torino (Italy)

A platform which enables expert users to create energymodels of urban areas to assess the current peformance of buildings and to develop plans and projects to improve thecurrent 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:

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

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.

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 scaleBuilding scale

Smart City Energy Assessment Framework

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

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

http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/SEMANCO.owl

http://purl.oclc.org/NET/ssnx/ssn

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.

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:


2. Smart cities


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

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

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 dataregarding 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

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

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

“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

“…….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

“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

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

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?

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

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]

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]

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?]


2. Smart cities


SEMANCO ‘s comprehensive approach:

1. Modelling energy efficiency problems with experts2. Structuring energy related data3. Creating an ontology of the urban energy

performance domain4. Creating an integrated platform:

• Integrating data and tools in a platform• Visualizing information• Analyzing data


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological

PlatformSEMANTIC ENERGY INFORMATION FRAMEWORK (SEIF)


Application domains

Stakeholders

WP3

WP5

WP4

Getting heterogeneous, distributed energy related data


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological



Application domains

Stakeholders

WP3

WP5

WP4


Modelling data with ontologies


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological



Application domains

Stakeholders

WP3

WP5

WP4



Providing tools and services to interoperate with data


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological



Application domains

Stakeholders

WP3

WP5

WP4




Using tools at different decision making realms


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological



Application domains

Stakeholders

WP3

WP5

WP4




Using tools at different decision making realms

Reducing carbon emissions


Energydata

Environmentaldata

Economicdata



tool


analysis tools





WP2

WP6

WP8

Technological



Application domains

Stakeholders

WP3

WP5

WP4

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).

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.

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).

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.

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.

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


DATA TOOLS

Smart City Expo World Congress, Barcelona, 18-20 November 2014

INTEROPERABILITY OF DATA AND TOOLS


DATA TOOLS




DATA TOOLS



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 owner1234567

Energy demand (kj. year)

234210

Index of multiple deprivation(u)

3

Apply filters

Reset filters

Number of buildings: 15322 / 50200

Total surface built: 9023 / 34342 m2

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 houseSurface: 4234Height: 23Floors: 5

CO2 emissions: 23523Energy consumption: 4234Energy demand: 32423SAP: 2345

IMD: 12Fuel poverty: 42%Income index: 32

LinkExport

intervention

SEIF + Semantic

energymodel

SEMANCO INTEGRATED PLATFORM

- Data: - Tools: - Users:

Experts’ knowledgecaptured in the ontologies

RDF data (semanticdata)

Urban energy model(GIS enriched withsemantic data)

Experts’sknowledgedescribe in Use Case and Activitiestemplates

Repositories(linked data or non-structureddata) of energyrelated 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

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 owner1234567

Energy demand (kj. year)

234210

Index of multiple deprivation(u)

3

Apply filters

Reset filters

Number of buildings: 15322 / 50200

Total surface built: 9023 / 34342 m2

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 houseSurface: 4234Height: 23Floors: 5

CO2 emissions: 23523Energy consumption: 4234Energy demand: 32423SAP: 2345

IMD: 12Fuel poverty: 42%Income index: 32

LinkExport

intervention

SEIF + Semantic

energymodel

SEMANCO INTEGRATED PLATFORM

- Data: - Tools: - Users:

Experts’ knowledgecaptured in the ontologies

RDF data (semanticdata)

Urban energy model(GIS enriched withsemantic data)

Experts’sknowledgedescribe in Use Case and Activitiestemplates

Repositories(linked data or non-structureddata) of energyrelated 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

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

6Case 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

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

6Case Study:

Newcastle

Case Study:

Manresa

Case Study:

Copenhagen

1




Output of a task


Informal Formal

3


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


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


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


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

6Case Study:

Newcastle

Case Study:

Manresa

Case Study:

Copenhagen

1




Output of a task


Informal Formal

3


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 - -

hasusable part of a building that is situated partly or entirely

below ground levelEN 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

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

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

6Case Study:

Newcastle

Case Study:

Manresa

Case Study:

Copenhagen

1




Output of a task


Informal Formal

3


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)

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

www.semanco-tools.eu


SEMANCO platform interface displaying the urban model ofthe Manresa city based on aerial images, terrain model andGIS data.

URBAN ENERGY MODELS, PLANS, PROJECTS

URBAN, BUILDING PERFORMANCE INDICATORS

VISUALIZATION MODES

FILTERS

INTEGRATED PLATFORM


Once a baseline reflecting the current state of the urban energy model hasbeen created, different visualiztion tools can be used to identify problemareas.

Cluster viewTable view

Performance indicators filtering

Multiple scale visualization

INTEGRATED PLATFORM


To determine the baseline(energy performance based onthe 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


3D model created after the GIS of the Manresa city

INTEGRATED PLATFORM : URBAN ENERGY MODEL


Creation of an Urban Energy Model



Selection of the tool for creating the baseline in the Urban Energy Model. Each toolincludes the regulatory framework, a general description, the methodology and thedata sources required by the tool.



After selecting the tool, the data sources can be personalized by the user



Finally, the users who are going to participate in the Urban Energy Model areselected.



Energy performance baseline of an urban area. Energy demand ofbuildings calculated with an energy assessment tool (URSOS) integratedin the platform.

INTEGRATED PLATFORM : URBAN ENERGY MODEL : BASELINE


information concerning the selected building which have not yet assessed

Building geometry obtained from the 3D model

Street address obtained fromGoogle Geolocation services

Performance indicators calculatedwith energy assessment tool

Year of construction obtained from thecadastre



Interface of the URSOS tool. The input data is automatically filled thanks to thesemantic integration of different data sources. Users can modify the input data in casethere are errors.



Interface of the URSOS tool. The input data is automatically filled thanks to thesemantic integration of different data sources. Users can modify the input data in casethere are errors.

Wall, ground and roofproperties from the buildingtypologies database

Year of construction from the Cadastre

Geometry obtained from the 3D model

Street address nameand Street view fromGoogle Geolocationservices

Ventilation from the buildingtypologies database



Results of the energy simulation carried out by URSOS



Creating plans to improve energy efficiency of buildings

INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS


Energy performance baseline of an urban area. Energy demand ofbuildings calculated with an energy assessment tool (URSOS) integratedin the platform.



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


Projects to apply improvement measures

INTEGRATED PLATFORM : URBAN ENERGY MODEL : PLANS : PROJECTS


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



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.

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.

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.

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

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

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

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.

www.eecities.com

www.semanco-project.eu

If you would like to have more information about this presentation,

please contact us:

[email protected]/arc