Application scenarios for the data generated from the Internet of Things are on the rise. For example, given the appliances’ energy consumption data, energy measurement tools now make it possible to save energy whilst efficiently controlling the consumption of different household devices. Yet, when the precise structured data describing appliance models is missing, it is difficult for such application scenarios to be realized. The developed OpenFridge ontology defines a basic vocabulary for the domain of measuring a refrigerator’s energy consumption, showing that the needed ontology schemata are already in place, but need to be identified and skillfully applied. Further, the ontology has been populated from the Web using data scraping, and the created dataset semantically describing the specifics of 1032 refrigerator models with 18665 triples, make these valuable assets for the development of further applications.

1. SELECTING ONTOLOGIES
AND PUBLISHING DATA OF ELECTRICAL
APPLIANCES: A REFRIGERATOR EXAMPLE
Anna Fensel, Fabian Gasser, Christian Mayr, Lukas Ott, Christina Sarigianni
Semantic Technology Institute (STI) Innsbruck, University of Innsbruck,
Austria
Contact: anna.fensel@sti2.at
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

2. Smart Grid is a Showcase for Data Economy
Smart Cities
Price Signals
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
Smart Grid
Operation
Energy Markets
Synchro
Phasers
Renewables
Parks
Compliance
Smart Buildings
Electro
Mobility
Smart
Metering
Smart
Appliances
Plant
Automation
Business
DSM
Compliance
Demand
Response
Capacity
Management
Prosumers
From general project presentation: http://www.slideshare.net/slotomic/big-data

3. Economy for Energy Efficiency Data
 What is energy efficiency?
– Using less energy to provide equivalent
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
service.
– A life-cycle characteristic of home
appliances.
(Knowledge)?
 How energy efficiency is being assessed?
– By measuring and comparison.
– EE of Design: Efficiency labels awarded by
– verification institutes.
– EE of Use: Best practices, comparisons
 How potential for increasing energy efficiency
is being assessed?
– By measuring/comparison  More context
needed
More info: http://www.atlete.eu,
http://eetd.lbl.gov/ee/ee-1.html
From general project presentation: http://www.slideshare.net/slotomic/big-data

4. A Value-chain for Energy Efficiency Data
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
 Metering (Data)
- A source of big data, two-way exchange
- Dynamic tariffs, distributed generation, demand
management
- Granularity of measurements aggregated vs.
appliance level
- Provides energy awareness context
 Energy Awareness (Knowledge)
- Awareness context vs. usage context
- Awareness at the energy service level needed.
- Smart-plugs for individual measurements
- Label is a decision support tool pointing to
technological improvements in energy efficiency of
appliances.  Efficiency Increasing Actions
- Appliance replacement, more efficient use, technology
improvements
From general project presentation: http://www.slideshare.net/slotomic/big-data

5. OpenFridge : Opening and Processing
Appliances Data for Energy Efficiency
 Building an ecosystem around data
 Developing a crowdsourcing platform for data
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
collection
 Exploring the concept of context-dependent energy
efficiency
 Combining (big) data and semantics for add-value
services
Improved
labeling
Improved
technology
and CRM
Better
decisions
about
replacement
and use
Home Users
Labeling Institutions
Manufacturers
Energy
Efficiency
Data
From general project presentation: http://www.slideshare.net/slotomic/big-data

6. Usage profile
avg.
consumption,
cooling cycle,
defrost
cycle,…
From Context to Recommendations
Measurements
power level (5s)
timestamp
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
Appliance profile
type, volume,
producer,
efficiency,
year of
production,
stand-alone/
built-in,
facing south,
location:
kitchen / cellar,
city, country,
number of users
Measurement profile
cooling level (1,2,3,..),
inside temperature, room
temperature, level of
filling,
doors opening events,
measurement duration
Comparisons, Recommendations & Analytics Services
Compare different refrigerators, refrigerators of the same type,
performance at different environmental conditions, set-points and
loadings, impact of opening the door, of aging, of installation, …
From general project presentation: http://www.slideshare.net/slotomic/big-data

7. Platform Enablers
 Hardware & service interfaces for data acquisition
- Currently based on the existing commercial system with
web-service interface
 Big data & analytics for data processing
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
- Anticipating large user base
 Semantic technology for value-add services
- Easy integration of external data, vocabularies and
ontologies from the ecommerce and energy efficiency
domain
- Logic-based reasoning
 Privacy and security protection of data
- Data provenance and veracity
 Community building and crowdsourcing
- Incentives based on high-quality recommendations
From general project presentation: http://www.slideshare.net/slotomic/big-data

8. OnTheMove Conferences, Meta4eS workshop, 28
October 2014
 Interfaces
Challenges
- Attractiveness and usability of user interfaces for data
acquisition
- Instrumentation for appliances data acquisition
- Privacy of user and appliances data
- Accuracy of data
 Big Data
- Analytics on raw data: mappers/reducers feed semantic
knowledgebase with model data
 Semantic Layer
- Ontology engineering
- External data integration
- Performance of the semantic knowledgebase
- Expressiveness of services via SPARQL queries for B2B/B2C
portal-based analytics
From general project presentation: http://www.slideshare.net/slotomic/big-data

9. Recommendations
&
Visualizations
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
Community &
Content Management
Big Data
Infrastructure
Data Acquisition
Web Service
Drupal Portal &
Web Service Client
Appliance Profile
Measurements
Profile
Appliance Profile
Measurements
Profile
Measurements
Business
Intelligence
Services
Users
Manufacturers
Labeling
Organisations
OpenFridge Architecture
Semantic
Knowledg
e
Base
Analytics
SPARQL: Data-as-
a-Service
Usage Profile
Volume?
Variety?
Velocity?
Veracity?
Value?
From general project presentation: http://www.slideshare.net/slotomic/big-data

10. OpenFridge Ontology – Main Classes
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

11. Semantic Annotation Process Overview
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

12. Tools for Data Fetching
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

13. Sources for Fridge Models Data
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

14. Results for Data Extraction
OnTheMove Conferences, Meta4eS workshop, 28
October 2014

15. Data Mapping – Implementation & Results
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
Tool: Python
• Importation process
• Restructure process
• Creation of the ontology-file
Result:
• OpenFridge ontology published at:
http://purl.org/opdm/refrigerator
• 1032 refrigerator models with 18665 triples
• OpenRDF-Workbench at www.openfridge.net

16. Lessons Learned
Technical:
● How to design an ontology 100% reusing other schemes?
● How to fetch Data from different HTML Websources?
● Screen scraping tools
● Creation of readable instances in protege
● How to get this data into a format that is readalbe for a tool like
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
protege?
○ How to develop?
○ Challenges
Organizational:
● Managing project (devide tasks)
● Meetings (how to communicate)
● Engagement

17. Current Actions and Next Steps
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
 Actions
- Interactions with the users
- Instrumentation @Home
- Privacy & data quality
 Data (Big Data)
- Efficient storage
- Analytic processing, data structures
 Semantic Processing
- Ontology Design
- Integration of external data from structured and
non-structured sources
- Development and optimisation of queries
(SPARQL) for added value servies
 User Tests
- Project partner internal (spring 2014)
- With test users & external (ongoing)
OpenFridge@WFF, Oct 2013
From general project presentation: http://www.slideshare.net/slotomic/big-data

18. Summary and Outlook
 Experiment in progress – take part in user trials!
 Our Goal: A platform for crowdsourcing of energy
efficiency data and a community for propagation of
energy efficiency social values
 Exploring the concept of context-dependent energy
efficiency:
- Measurements in a broader context of different usage
parameters within a community of users
- Providing necessary explanations to motivate corresponding
users’ actions towards improving the energy efficiency of
services
 Integrating Big Data and semantic technology
- Maintaining large volumes of raw data, analytics to transform
raw data into the parameterized information
- Developing appropriate ontologies to link parameterized
energy efficiency information with the usage context
information
 Developing semantic-based delivery of add-value
From general project presentation: http://www.slideshare.net/slotomic/big-data
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
services
- Querying and reasoning
 Focusing on refrigerators as they are the largest energy

19. Join via: www.openfridge.net
Thank you for your attention!
Questions?
References:
• Fensel, A., Gasser, F., Mayr, C., Ott, L., & Sarigianni, C. (2014). Selecting
OnTheMove Conferences, Meta4eS workshop, 28
October 2014
Ontologies
and Publishing Data of Electrical Appliances: A Refrigerator Example. In On the
Move
to Meaningful Internet Systems: OTM 2014 Workshops (pp. 494-503). Springer.
• Tomic, S., & Fensel, A. (2013, October). OpenFridge: A platform for data
economy
for energy efficiency data. In IEEE International Conference on Big Data (pp. 43-
47). IEEE.