Ontology-based IoT and Building data Integration for Demand … · 2019-10-28 · Ontology-based...

© IK4-TEKNIKER 2019


ETSI IoT Week • Sophia Antipolis, 24th October 2019

Ontology-based IoT and Building data

Integration for Demand Response

Iker Esnaola-Gonzalez

This presentation is co-funded by the European Commission under the “Integrated demand REsponse Solution towards energy POsitiveNeighbourhooDs” project under Grant agreement No. 768619


01 The RESPOND Project

02 Building and IoT Data Integration

03 Pilot House Example

00 Motivation

-2-

04 Conclusions





00 Motivation

-3-

04 Conclusions


Peak Energy Demandsare Undesirable


-5-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME

ENERGY SUPPLY ENERGY DEMAND

Grid operators deploy generationplants to satisfy demand peaks


-6-

Demand Response (DR) as a Solution


-7-

Demand Response (DR) as a Solution

• Change user consumption habits• Balance energy supply and demand• Via incentives


-8-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME


DR tries to shift energy use tobalance supply and demand


-9-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME




-10-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME




-11-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME




-12-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

ENER

GY

TIME




Demand Response ispromising for theResidential Sector


-14-

Renewable Energy’sExploitation Difficulties


-15-

Renewable Energy’sExploitation Difficulties

• Intermittent• Dependent on the weather


-16-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00

ENER

GY

TIME

PV GENERATION ENERGY DEMAND

Renewable Energy does not match thedistribution of energy demand in time


-17-

6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00

ENER

GY

TIME

PV GENERATION ENERGY DEMAND

Renewable Energy does not match thedistribution of energy demand in time





00 Motivation

-19-

04 Conclusions


ARAN ISLANDS, Ireland

MADRID, Spain

RESPOND Pilot Sites

AARHUS, Denmark


-21-

Towards the user-engagement:

• Multilingual

• Cross-platform

• Functionalities including display and

actuation

RESPOND Mobile App



-22-

Data Analytic Services

• Energy Demand Forecasting

• Energy Production Forecasting

• Suggest Optimal DR opportunities



-23-

Integrating heterogeneous IoT data

• Monitoring & Actuation Devices

• Appliances

• Energy Prices

• Weather Forecast






00 Motivation

-24-

04 Conclusions


02 Building and IoT Data

-25-

Building and IoT Data Storage

Building Topological Data IoT Data


-26-

Building and IoT Data Storage

Building Topological Data IoT Data



-27-

BOT SEAS Ontology SAREF

RESPOND Ontology:

Reusing Existing Ontologies



-28-

RESPOND Ontology:

Defining New Concepts

• Extending existing ontologies• New Appliances (e.g. Tumble Dryer)

• New Spaces (e.g. Common Area)

• New Qualities (e.g. Gas Consumption)

• New Units (e.g. ppm)

• Creating new concepts• Building-related properties (e.g. Volume)

• Linking Measurements with DB Queries

• Linking Devices to Gateways



-29-

https://w3id.org/respond

RESPOND Ontology




-30-

• Available online

• CC-BY 4.0 License

• Documentation Page

• Recommended Metadata


RESPOND Ontology







00 Motivation

-31-

04 Conclusions


-32-

Pilot Site Characterization

• Filled by each Pilot Site Coordinator

• Excel sheets with installed devices’

information:

• Device Type

• Location

• Connected Appliance

• …




-33-

RESPOND Ontology

Instantiation


-34-

Linking devices with

their measurements

stored in another Database

respond:hasDBQuery



-35-

Mobile App Data Display:

Dishwasher Energy Consumption



-36-

LDAP Directory:Data Access via hierarchical directory service

phpLDAPadmin ServerV0.8.0


OpenLDAP Serverv1.2.5


-37-

Semantic Repository:Information Retrieval via SPARQL Query

Openlink Virtuoso Server v07.20.3217



-38-

Time Series Database:

Information Retrieval via InfluxQL Query

InfluxDBv1.6.2



-39-

Mobile App Data Display:

Dishwasher Energy Consumption

• Web Service

• Enables data-access control

• Decoupling from Repositories

• Enables Distributed Data Store

• Scalability is eased

VirtuosoServer

InfluxDBServer

PROXY REST Web Service


SecurityServer





00 Motivation

-40-

04 Conclusions


04 Conclusions

-41-

Conclusions

• Linking Semantic Technologies with

IoT Technologies

• Leveraging existing ontologies such

as SAREF, SEAS, BOT instead of

developing new ontologies

• Not exclusive for Energy Domain


Thank you for your attention

Iker Esnaola-Gonzalez

[email protected]

This presentation is co-funded by the European Commission under the “Integrated demand REsponse Solution towards energy POsitiveNeighbourhooDs” project under Grant agreement No. 768619

mailto:[email protected]

Ontology-based IoT and Building data Integration for Demand … · 2019-10-28 · Ontology-based...

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