Energy Positive Districts/Smart Cities WorkshopWorkshop

Data ModelsData Models2 October 2014

Nice

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Outline of the session

1 REQUIREMENTS FOR DATA MODELLING1. REQUIREMENTS FOR DATA MODELLING2. READY4SMARTCITIES3. URB-GRADE4. INDICATE4. INDICATE5. WHAT COMES NEXT?

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Data management in smart cities• Cross-organisational data management

ff

Data management in smart cities

– Different stakeholders need to share their data – Different/multiple domains of interest

• Requirements for data sharing will be different depending on:p g– The type of stakeholder (e.g., governments,

companies individuals)companies, individuals)– And its individual interests

H t it i ICT i d t d t• Heterogeneity in ICT required to manage data– Discover, understand, integrate, communicate

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A new generation of dataA new generation of data

• Decentralized and distributed • Decentralized and distributed • Across:

i ti – organizations, – sectors,

b d d – borders, and – languages

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• Static and dynamic (e.g., streams)

Requirements for data modellingRequirements for data modellingDATA ARE ONLINE

• Documents + services • From a Web of documents to a Web of Data • Semantic content is accessible to humans but not

(easily) to computers

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(easily) to computersImage: http://www.business2community.com/social-media/three-things-that-all-social-networks-have-in-common-0566559

Requirements for data modellingRequirements for data modellingDATA ARE HETEROGENEOUS

Different domains • Different domains • Different perspectives

ti l – spatial – temporal

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• Different scales and viewpoints

Requirements for data modellingRequirements for data modellingDATA HAVE CONTEXT

KPI

C• Crucial to provide humans and machines with additional information D d b i d b l • Data need to be accompanied by contextual information

E lit li i t t

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– E.g., quality, licensing, provenance, trust

Requirements for data modellingRequirements for data modellingDATA ARE NOT INDEPENDENT

“What was the effect in terms of CO2 emmisions of the trafficCO2 emmisions of the trafficjams caused by the final match of the Basketball WorldChampionship?”Championship?

• Complex questions cannot be answered by data:Complex questions cannot be answered by data:– from one domain– from one data source

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– from one data source

Ontologies: Shared terminology and semantics Ontologies: Shared terminology and semantics GOAL: TO SHARE AND REUSE DATA MODELS

• Knowledge representation – Shared conceptual model of the world

Describes (simple or complex) interactions between different domains – Describes (simple or complex) interactions between different domains – Ontological commitments are explicit in the schema

• Formal specification of semantics I f ti b h d ith t l f i – Information can be exchanged without loss of meaning

– Similarities and differences between data are explicit • Reasoning

– Allows inferring new information from existing data – Allows checking consistency of schemas and data

• Knowledge reuse g– Shared schemas

• Support communication – Between people between applications and between both

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Between people, between applications, and between both

A new generation of ontologiesA new generation of ontologies

• Describe complex interactions between different domains domains

• Reuse generic domains (e.g., time, process, measurement)measurement)

• Model information at different scales and viewpoints• Reconcile expressivity at different levels

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Current practice• Use open and well-established Web standards

– For data (RDF), ontologies (OWL), querying (SPARQL), APIs, (LDP),

Current practice( ), g ( ), q y g ( ), , ( ),

etc. • Formal semantics

– The degree of formalization depends on particular needs g p p– Usually lightweight ontologies

• Ontological commitments – No need for losing control over your own schema or data No need for losing control over your own schema or data

• No need for a global agreed schema – Plenty of interrelated small ontologies

Consensual vocabularies support avoiding alignment problems – Consensual vocabularies support avoiding alignment problems – Industrial standards must not be disregarded

• Ontology development trends C ll b ti t l d l t – Collaborative ontology development

– Reuse of rich knowledge resources – Reuse of ontologies

C t t l i t t t k

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– Connect ontologies to create networks

READY4SMARTCITIESREADY4SMARTCITIES

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READY4SMARTCITIESREADY4SMARTCITIESOBJECTIVES

• ICT Roadmap and Data Interoperability for Energy Systems in Smart CitiesSystems in Smart Cities– Data Interoperability: identify energy-related

vocabularies and ontologies towards dynamic and vocabularies and ontologies towards dynamic and interoperable Energy Management Systems

– ICT Roadmap: identify needs of ICTs towards holistic, p y ,planning, design, construction and operation of energy systems for Smart Cities.

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READY4SMARTCITIESREADY4SMARTCITIESRESULTS

• Ontology and dataset catalogues:42 Ontologies – 42 Ontologies

– 9 Datasets Ontology alignments catalogue• Ontology alignments catalogue

• Guidelines for data publication

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READY4SMARTCITIESREADY4SMARTCITIESRESULTS

• Vision and Preliminary Roadmap (1/2):From Citizens to Prosumer > informed and active;– From Citizens to Prosumer -> informed and active;

– BMS transform Buildings into connected objectsEnergy sector systems are interconnected with BMS– Energy sector systems are interconnected with BMS.

– Municipality integrates other dimensions in the energy efficiency equation efficiency equation …

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READY4SMARTCITIESREADY4SMARTCITIESRESULTS

• Vision and Preliminary Roadmap (2/2):Linked data / Big Data– Linked data / Big Data

– Communication protocols, data models and standards for all ICT communication between energy system nodesall ICT communication between energy system nodes.

– Security and privacy aspects to prevent from any breach or leak.

– Internet of Things: easy to use and to interconnect to each other. It means the interoperability issues have to p ybe solved from the hardware level up to the semantic level.

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READY4SMARTCITIESREADY4SMARTCITIESNEXT STEPS

• Collection of ontologies and datasets;• Definition of scenarios at the different levels;• Definition of scenarios at the different levels;• Validation of our roadmap against the community;• Evaluation against current collections;

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URB-GRADEURB GRADE

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URBGRADEURBGRADEOBJECTIVES

• Creation of a flexible data model for energy efficiencyefficiency

• This data model was previously designed in Oddysseus project And enriched with the Oddysseus project. And enriched with the Collaboration with other on-going European research project: research project:

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URBGRADEURBGRADERESULTS: CORE NODE (ENODE) AND DEPC

• Enode (Energy node)A node in a energy network that consumes produces – A node in a energy network that consumes, produces and stores energy

– Enodes can be networks itself with sub Enodes and sub Enodes can be networks itself with sub Enodes and sub Econnections

• dEPC(dynamic energy profile card) dEPC(dynamic energy profile card) – Functional and Technical description of an Enode

• Static information: Persistent propertiesStatic information: Persistent properties• Dynamic information: Properties that may change

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Considered key scalable pillars

ProfilesProfiles

CORE e-Node + dEPC

eConnections KPIs+ dEPC

Messages

e-Nodespecialization

g&

Sensed Dataspecialization

URBGRADE URBGRADE RESULTS: EXAMPLE

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URBGRADEURBGRADESUMMARY

• Data model already created but continues evolving• Flexibility• Flexibility• Topology• Energy exchange among energy nodes • Aggregated Information and gg g• Added Value.

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INDICATEINDICATE

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INDICATEINDICATEOBJECTIVES

• Develop a Virtual City Model (VCM) – 3D model which acts as a visual aid– database to illustrate a city and store processed outputs from

the Dynamic Simulation Model and Indicator results.Th VCM ill• The VCM will:– Deliver a centralized platform to allow efficient and, where

possible interoperable import/export of the VCM core datapossible, interoperable import/export of the VCM core data– Provide seamless integration of real and simulated data from

the test cities into the VCM– Enable the INDICATE product to analyse the core data and

empower stakeholders to make rapid informed choices on multiple user generated scenarios

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p g

INDICATEINDICATERESULTS

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INDICATEINDICATERESULTS

• Architecture vision developedThe software product– The software product

• What the users will work with– The service productsThe service products

• What feeds into the cloud– The cloud database

• Working in the background

• Format i3s consideredFormat i3s considered– For transfer of 3D geometry data between platforms

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INDICATEINDICATENEXT STEPS

2D data passed to

• VE back end using this to passed to

VCMthis to extrude 3D model

3D data passed

into VCM

• Used directly by simulation back endinto VCM back end

Then passed to front end

• ESRI CityEngine

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INDICATEINDICATENEXT STEPS: GENERATION FROM 2D FOOTPRINT

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What comes next?• What requirements do I have for modelling data?

Wh t i t ill I h ?

What comes next?

• What requirements will I have?• How to develop ontologies? • How to reuse other ontologies? • Why do I need to make an ontology?

B i hb i d i it– Because your neighbor is doing it

Don't need smarter applications, need smarter dataneed smarter data

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More informationMore information

• URB-Grade: http://urb-grade.eu

• INDICATE: http://www.indicate-smartcities.eu/

• Ready4SmartCities: http://www.ready4smartcities.eu/

THANK YOU FOR ATTENTION!

ANY QUESTIONS?