ict for smart cities

LD4SC Summer School 7th -‐ 12th June, Cercedilla, Spain

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1st Summer School on Smart Ci2es and Linked Open Data (LD4SC-‐15)

ICT for Smart Ci2es

Dr. Dimitrios Tzovaras InformaBon Technologies InsBtute (ITI)

Centre for Research and Technology Hellas (CERTH)


Index

•  Smart Ci2es are a Necessity – CiBes in Numbers – EU Targets for Climate and Energy

•  The Concept of Smart CiBes •  ICT Key Enabler for Smart CiBes •  ICT in the EeB Sector of Smart CiBes •  European FP7/Horizon ICT projects •  Future Vision

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Smart CiBes are a Necessity (1/2)

–  The percentage of people over the age of 60 increases each year

–  The number of people over the age of 60 is expected to triple by 2050 and will outnumber children under 15

Ci#es in numbers

Urban Growth

Global Warming

Ageing Popula#on

–  Urban popula2ons will grow by an es2mated 2.3 billion over the next 40 years

–  The World Health Organiza2on es2mates, the global urban popula2on will grow approximately 1.84% per year between 2015 and 2020

–  Ci2es consume 75% of the world’s energy –  Ci2es produce 80% of its greenhouse gas emissions

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Smart CiBes are a Necessity (2/2)

20 % 20 % 20 %

EU Targets for Climate and Energy

By 2020

40 % reduc2on in gas

emissions

27% increase in

renewable energy

30% improvement in energy efficiency

By 2030

Image taken from: hZp://ec.europa.eu/energy/en/topics/energy-‐strategy/2030-‐energy-‐strategy

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Index

•  Smart CiBes are a Necessity •  The Concept of Smart Ci2es

– Challenges as New OpportuniBes –  Smart CiBes in Europe

•  ICT Key Enabler for Smart CiBes •  ICT in the EeB Sector of Smart CiBes •  European FP7/Horizon ICT projects •  Future Vision 5


The Concept of Smart CiBes (1/4)

Challenges as New Opportuni2es

Smart Ci2es Defini2on “ci%es that u%lise informa%on and communica%on technologies with the aim to increase the life quality of their inhabitants while providing sustainable development” (Bakici, Almirall, & Wareham, 2013, p. 137)

Solu#on Efficient and innovaBve technologies to meet future challenges

Image taken from: hZps://ec.europa.eu/digital-‐agenda/en/smart-‐ciBes

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The concept of Smart CiBes (2/4)

Image taken from: www.districtoffuture.eu

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The concept of Smart CiBes (3/4) The Smart City Concept is emerging as the ü  base of sustainable growth ü  op2mal solu2on of the world’s challenges

Fact The European InnovaBon Partnership on Smart CiBes and CommuniBes

o  Integrates ICT, energy and transport o  Establishes strategic partnerships between industry and European ciBes for

urban systems and infrastructures of tomorrow

Image taken from: hZp://ec.europa.eu/eip/smartciBes/files/eip-‐ifc-‐infographic.pdf

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CiBes Commitments in Energy, Transport, Mobility and ICT

The concept of Smart CiBes (4/4)

InformaBon Resources: hZps://eu-‐smartciBes.eu/ hZp://www.europarl.europa.eu/studies

The raBo of Smart CiBes to SmartCity iniBaBves across the EU

SmartCiBes by Country

Smart Ci2es in Europe

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Index

•  Smart CiBes are a Necessity •  The Concept of Smart CiBes •  ICT Key Enabler for Smart Ci2es •  ICT in the EeB Sector of Smart CiBes •  European FP7/Horizon ICT projects •  Future Vision

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•  The Europe 2020 strategy incorporates a commitment to –  promote the development of Smart Ci2es throughout Europe –  invest in the necessary ICT infrastructure and human & social capital

development –  increase effecBveness, reduce costs and improve quality of life through ICT

technology •  ICT itself accounts for 2% of all CO2 emissions but a vast potenBal to help

improve the energy efficiency of other sectors

ICT Key Enabler for Smart CiBes (1/8)

InformaBon and communicaBons technology (ICT) is a key enabler for smart city innovaBon

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In Smart Ci#es •  Digital technologies translate

into: –  BeZer public services for ciBzens –  BeZer use of resources and –  Less impact on the environment


Increased Energy Efficiency Maximum Interoperability

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ICT pivotal role

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•  Broadband Networks ü  Access to Internet and public

services for maximum connec%vity ü  Wireless broadband for mobile

applica%ons, smart devices, sensors, IoT apps

•  Embedded Systems ü  Smart Devices ü  Sensor/Actuators for remote

monitoring and control ü  Embedded Intelligence (spa%al,

environmental, etc.)



•  Connected ICT Infrastructure ü  Connec%ng smart devices, sensors and

applica%ons for ubiquitous ICT facili%es ü  Data processing and op%mal decision

making •  Data Modelling and Linked Open Data

ü  Ontologies and Vocabularies for data processing

ü  Publica%on of Linked and Seman%cally enhanced Data

ü  Build on top of web-‐based Applica%ons and Services

ICT pivotal role

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•  The Internet of Things (IoT) seamlessly incorporates a large number of heterogeneous end systems ü  Open access to data for the development of digital services ü  Urban IoTs uBlise communicaBon technologies to support added-‐

value services for the ciBzens and administraBon

! a plethora of machines, devices, sensors, actuators, and other objects are interconnected to each other and to higher-‐level systems exchanging data

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“1.1 billion connected things will be used by smart ci%es in 2015 rising to 9.7 billion by 2020 ”

Gartner, Inc.


Image taken from: www.channelnomics.com

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Context – aware sensors and networked devices

Data extracBon,processing, storage, and analyBcs at distributed points

Central data collecBon with integrated and open-‐standard APIs for building on open-‐data

On top applicaBons and services for ciBzens & administraBon

Smart City ICT Architecture


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Main targets – CiBzens – Building sector – Energy sector – Municipality

Smart City Layers using ICT applica#ons

! Energy sector is closely interconnected with the Building sector at a city scale system represen%ng distributed nodes of an interlinked network


Index

•  Smart CiBes are a Necessity •  The Concept of Smart CiBes •  ICT Key Enabler for Smart CiBes •  ICT in the EeB Sector of Smart Ci2es •  European FP7/Horizon ICT projects •  Future Vision

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ICT in the EeB Sector of Smart CiBes (1/2)

•  Energy efficient (nearly zero, net zero, and energy posiBve ) buildings with on-‐site renewable energy producBon

•  Intelligent Buildings are connected with the energy networks and are dynamically responsive

•  BMS & Tools for managing the building as an ac2ve prosumer in the city’s energy system

•  Huge amount of dynamic data derived from sensors, smart devices etc. are processed for paZern recogniBon (energy behavior, usage paZerns, occupancy etc.) and predicBon R4SC Vision

General Aim The Urban mulB-‐domain Intelligence

System is to be based on a standardized ontology framework providing a semanBc interoperability and common data format

Smart Buildings Characteris#cs

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Requirements •  Interoperability to be ensured at different levels

–  physical level: the sensors, actuators, and acquisiBon systems are interconnected

–  communicaBon protocols, data structures and seman2cs are shared

•  BEM systems operate with Standardized Data Models ( e.g. buildingSMART IFC standard)

• Smart appliances communicate with open protocols (standards, dicBonaries, etc.)

ICT in the EeB Sector of Smart CiBes (2/2)

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•  Smart CiBes are a Necessity •  The Concept of Smart CiBes •  ICT Key Enabler for Smart CiBes •  ICT in the EeB Sector of Smart CiBes •  European FP7/Horizon ICT projects

– ADAPT4EE FP7 ICT Project – INERTIA FP7 ICT Project – SWIMing Horizon 2020 Project

•  Future Vision

Index

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Smart Buildings Energy Efficiency

Applica#on-‐Based ICT developments

Stakeholders Engagement Standardiza#on Best Prac#ses Use Cases

Evolu2on to the New Data Ecosystem

FP7/Horizon 2020 EU ICT Projects

•  ADAPT4EE & INERTIA use case paradigms for the uBlisaBon and development of semanBc vocabularies & data

•  R4SC & SWIMing Community building projects

–  IdenBfy use cases –  Aggregate applicaBon projects

results –  Deliver best pracBses for

ontologies and datasets towards a common approach

European FP7/Horizon ICT projects

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The ADAPT4EE FP7 ICT Project

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ADAPT4EE -‐ Overview Adapt4EE aims at augmen2ng the contemporary architectural envelope by incorpora2ng business and occupancy related informa2on to the early construcBon products. The project provides a holis%c approach to the design and evalua#on of the energy performance of construc%on products at an early stage and prior to their realiza%on..

Construction & Design

Real Life(Pilot Facilities)

TRAINING

Adapt4EE Enterprise Building Data Modeling

(eeBDM)Business Process Model

Occupancy Model

SIMULATION

Monitoring – Real LifeData Acquisition

& Analysis

Energy Performance Evaluation & Visualization

Building Information

Model

Designers & Engineers

Towards Energy Efficient

Building Design

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Occupancy and Business Modelling in Smart Buildings

•  Building performance analysis highly depends on occupants behavior •  Building Occupancy Analysis helps predicBng space u2liza2on and

energy usage with increased accuracy, granularity and reliability •  Lack of models that fully characterize occupancy behavior with the

Enterprise/Organiza2on that will be housed in the facility/building under design

•  Enrichment of exisBng BPS tool with occupancy paZerns & individual user profiling

energy consump#on space u#liza#on

Final Aim

Predic2on

from the early design phase

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AcBvity Based and Behavioural OM Methodology (1/3)

Step1. Defini2on of Occupancy Models in a Building

Occupant Behavior Presence & Movement

When What Where

Who Why

Organiza#on / Enterprise Model (BPM)

Building Model Elements (BIM)

Space Equip. Env. …

Actor Business Process

Assets

Step 2. Contextual Analysis of users’ dynamic behaviour fully integrated with BIM model

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Step 4. User Behavior Models to semanBcally enrich Occupancy Models with informaBon related to acBviBes performed during human presence in building

Step 3. Incorpora2on of several domain models to cope with BIM and BPM aspects


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Step 5. Business Process Models to correlate the acBviBes with the enterprise/organizaBon that will be “housed” in the building under design


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Business/Enterprise Data Models

•  Enterprise Data Models that can be used in Building Performance Analysis Tools

(actors, roles, units, business processes, equipment &semanBc relaBons among them)

Business Process X

v  Actors involved v  Sequence of Ac%vi%es performed by Actors v  Occupants’ interacBons with Enterprise Resources v  …

DefiniBon of primary business processes (Skeleton AcBviBes)

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Combining Behavioural & Business Models (1/3)

•  The combined seman2c model defines basic interac2ons among the building (BIM), the enterprise (BPM) and occupant’s domain models

•  Denotes the loca2ons (Space) that ac2vi2es (AcBvity) can be executed by the occupants (Actor) of the building under design

•  Depending on the level of development, the data schema allows for rela2ng BIM components (equipment, lighBng and HVAC, etc) with ac2vi2es and actors

•  Fully extendable data schema that can incorporate more parameters for occupant behavior simulaBon and building performance analysis

User AcBvity Schedules

Data Views

Performance Analysis

Occupancy Schedules

Occupancy related Loads

Control Behavior Predic2on Seman2c Model

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XML schema as an extension to gbXML standard

Enterprise/Business Model

Occupancy Behavioural Model


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•  OWL Data Schema easily incorporated to exisBng BIM models standards

ADAPT4EE Results (1/6)

•  Ontologies and XSD Schemas available on Web via ADAPT4EE official website

h\p://www.adapt4ee.eu/adapt4ee/results/ontologies.html

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ADAPT4EE Results (2/6) ADAPT4EE CIM High Level View


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•  Measurement Ontology


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•  Building Ontology

•  Process Ontology



•  Occupancy Ontology Allowing semanBc interpretaBon of raw measurements related to occupancy

profiles –  Support for reasoning on BIM, BPM and contextualized EVENTS (BIM,

device/equipment) –  A working example for UNAV pilot site has been included for the usage of

the ontology

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•  StaBc and Dynamic occupancy-‐related parameters extracted by the Building Measurement Framework –  Support for extracBng Occupant presence, behavioral, schedule and

comfort related aZributes/parameters –  Enabling correlaBon of BIM and BPM related parameters


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ADAPT4EE ApplicaBons using the Data Models

•  Occupancy profiling within Adapt4EE –  Based on the analysis of the

foreseen Building Sensor Cloud (depth, RFID, temperature, CO2 , humidity, lighBng, etc)

•  Usage of Occupancy data models in Measurement phase (calibraBon/training) –  AcBvity mapping –  GeneraBon of behavioral templates for use in simulaBon framework (Combining both real measurements and defined BPM models )

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INERTIA -‐ Overview

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INERTIA FP7 ICT Project INERTIA project addresses the "structural iner#a" of exis%ng Distribu%on Grids by introducing more ac#ve elements combined with the necessary control

and distributed coordina#on mechanisms. To this end INERTIA will adopt the Internet of Things/Services principles to the Distribu%on Grid Control Opera%ons.

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•  Demand Side Management Framework –  IntegraBon of local demand and generaBon (DER) into the Grid –  Local (Building) & Global (Grid) level mulB-‐agent management

•  IntegraBon of Seman#cally Enhanced Distributed Energy Resources (DERs) profiles

•  Service Oriented Middleware for mulB-‐direcBonal communicaBon

•  HolisBc Energy Performance Models •  Prosumer Seman#c Profiles

–  IncorporaBng behavior, acBvity flows and environmental knowledge

INERTIA -‐ Overview

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INERTIA – Pilot Site MulB-‐sensorial Network

Building Pilot Site for Data Acquisi2on •  CERTH (Greece)

–  InstallaBon of the Building AutomaBon System in a set of pilot areas

–  ConsumpBon/ProducBon DERs •  Mul2-‐sensorial Network for data

Acquisi2on –  Environmental Measurements –  Power ConsumpBon –  Occupancy –  User Profiling

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•  Based on the LinkSmart Middleware –  Layer to integrate a set of heterogeneous devices and sensors

–  Device Managers supporBng 20 different types of DERs, sensors and actuators deployed on 7 different networking plarorms

–  Common approach for data and control acBons presentaBon for interoperability

•  Seman2c models created in form of ontologies –  Containing spaBal, DER and occupancy aspects

–  Inference mechanisms applied –  INERTIA semanBc devices to adress complex models

LinkSmart Event Data Model

INERTIA – Data Models

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INERTIA Building Ontology (1/3) •  INERTIA Ontology describes three main domains addressed in the building context:

ü Loca2on ü Devices ü Occupants

•  Refers to the Energy-‐related BIM model and correlates Distributed Energy Resources with Sensors/Actuators

•  Describes DERs such as HVACs, Lightning etc. •  Describes the WSN with a wide set of Sensor/Actuator sub-‐classes represenBng the installed equipment.

•  Applica2on-‐based ontology to cover specific needs of the project

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INERTIA Building Ontology (2/3)

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•  RelaBons between each two levels give locaBon hierarchy

•  Hierarchical dependencies between connected levels

•  LocaBons mapped to sensors, DERs or occupants.

INERTIA Building Ontology (3/3)

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INERTIA-‐ SemanBc rules sample model

•  Sample case ü  Retrieve all events of

temperature sensors located at the 1st floor of the building

ü  SpaBal hierarchy ü  4 devices, 1 smart

plug and 3 thermometers with unique IDs

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•  Occupancy aspects affect the usage of specific devices (e.g. Office and Home Appliances)

•  The occupancy modelling is combined with occupancy detecBon systems, i.e RFID, to beZer correlate device applied acBons with human presence

•  Occupancy Modelling Concept ü  Each Occupant is correlated with

personal appliances

ü  Each Occupant is correlated with a loca%on

INERTIA Occupancy Ontology

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INERTIA Results •  OWL Data Schema easily incorporated to exisBng BIM

models standards •  INERTIA Linked Data endpoint available for SPARQL

quering h\p://www.iner%a-‐linkeddata.i%.gr

•  Ontologies XSD Schemas and Dataset Instances available on Web via INERTIA official website

h\p://www.iner%a-‐project.eu/iner%a/results/ontology.html

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CollaboraBve Efforts for StandardizaBon (1/3)

ETSI, the European Telecommunica%ons Standard Ins%tute, standardized the machine-‐to-‐machine (M2M) architecture This architecture describes in which way and how machines interact with one another

TNO study (January 2014 -‐ March 2015) for the EC ü Brings together semanBcs and data from smart appliances in buildings and households ü  Smart Appliances reference (SAREF) ontology that can be used to match the data from different organizaBons. ü SAREF ontology fits into the ETSI M2M architecture

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•  CollaboraBon with ETSI Board SAP and DG Connect during the 4th Workshop on Smart Appliances

•  Main focus on bringing together semanBcs and data from smart appliances in buildings and households

•  Output the Smart Appliances reference (SAREF) ontology

•  INERTIA Ontology mapped with SAREF Ontology to idenBfy commonaliBes and future extensions

Available at: hZps://sites.google.com/site/smartappliancesproject/ontologies/reference-‐ontology

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Mapping of different Device Concepts and Project Ontologies


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SAREF Ontology

The SAREF ontology focuses on the concept of device

Device class and its proper#es

Building Space and Building Object classes

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INERTIA Ontology Comparison Example with SAREF Ontology (1/4)

Light Actuator INERTIA Configura2on Requirements

SAREF Ontology

Type (e.g. ON/OFF, dimming etc.) • saref: hasModel string • saref:hasCategory saref:DeviceCategory

Measurement range Event repor%ng interval

• saref:Open_close_func%on • saref:Metering Func%on

Loca%on of actuator • saref:isLocatedIn saref:BuildingSpace (BuildingSpace class contains the Device and the space type FIEMSER Ontology)

Func%ons (ON/OFF set & status monitor, consump%on)

• saref:hasFunc%on saref:Open close Func%on saref:On_off_func%on saref:Sensing Func%on

Opera%on Status ON/OFF saref: hasState saref:state

Consump%on value saref: Power

Dimming Factor rate (if applicable) No dimming factor. Possible extension dimming factor state

Light Switch

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Visual Representa2on of SAREF Ontology

INERTIA Ontology Comparison Example with SAREF (2/4)

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Visual Representa2on of Light Switch


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Visual RepresentaBon of Metering Func#on accomplished by Light_Switch

Visual RepresentaBon of Open_Close_Func#on accomplished by Light_Switch

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Proposed Extensions According to INERTIA (1/2)

•  INERTIA Smart Dishwasher to operate as Smart Energy Appliance according to specific operaBon profiles

•  Wide variety of services: from simple awareness up to a fully integrated Energy Management System

Proposed Extensions •  BIM allocaBon of the Dishwasher •  The Dishwasher properBes should be retrieved via

GET command •  Extra Commands to be applied;Washing start

%me, Phase delay %me

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•  HVAC system consisBng of external (VRV units) and internal units

•  BIM allocaBon of both external and internal units

•  External units correlated with their internal units

•  AddiBonal Commands to be applied

INERTIA HVAC system Proper2es ConfiguraBon parameters

Current OperaBonal Status (ON/OFF)

Current Power ConsumpBon

Actual Temperature

Temperature Set Point

User Control On Off (ACTIVE/PASSIVE)

User Control Temperature Set Point

INERTIA Genera2on Units Proper2es ConfiguraBon Parameters

ConnecBon status

AcBve Power Set point

Current AcBve Power/capacity

Charging status

Producing or Consuming mode (only for storage devices)

•  Genera#on/storage devices to be included ü  Photovoltaic panels ü  BaZeries (EVs, PVs) ü  Power Generators

•  AddiBonal Commands to be applied

Proposed Extensions According to INERTIA (2/2)

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Possible Extensions to be Standardized

–  Extension of available subclasses to address a wider variety of instances (complex devices, HVAC system, genera#on units)

–  Parameters such as Temperature, u#liza#on rates, could affect Energy profiles of other devices i.e. HVAC systems, thermosta%c appliances

–  Extension of Measurement Units (Humidity units, C02 rate, occupant units etc.) for environmental and occupancy sensors

–  Some properBes could be more generic (e.g. saref:hasDevice) to address the concept of different sensors/actuators ahached to DERs

–  Extension of available func%ons/services (e.g. add phase delays for Washing Machines)

–  Occupancy aspects and user behaviour to device could be taken into considera%on

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z


1st Summer School on Smart Ci2es and Linked Open Data (LD4SC-‐15)

Thank you for your aZenBon!

ict for smart cities

Science

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percentage of people

number of people

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