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CLICK TO EDIT MASTER
TITLE STYLE
A semantic approach towards
implementing energy efficient
lifestyles through behavioural
...
Outline
• ENTROPY Project
• Introduction
• Related Work
• Motivation
• Reference Architecture
• Semantic Models
– IoT-Ener...
• The project aims to design and deploy an innovative IT ecosystem
targeting at improving energy efficiency through consum...
• Buildings are responsible of 41% of total energy consumption in
Europe in 2010, followed by transport (32%), and industr...
• SEMANCO [3]
– An ontology-based energy information system for enabling stakeholders
to make guided decisions on how to r...
• Current literature accommodates an abundance of applications that
facilitate semantic technologies
• However, they mostl...
• Fernando is an ENTROPY user
• In a summer day, he comes to his
office and runs the air-conditioning
• Based on the data ...
Reference Architecture
www.sti-innsbruck.at 7
• IoT-Energy Monitoring Ontology
– An ontology to represent energy
infrastructure of buildings and
sensor observations as ...
• IoT-Energy Semantic Model mainly borrows concepts from the
following ontologies
– Smart Appliances REFerence (SAREF) [6]...
IoT-Energy Ontology (2)
www.sti-innsbruck.at 10
An excerpt of IoT-Energy Ontology
• Core concepts of the Behavioural Intervention Ontology
– An Intervention in different forms (e.g. list of tasks, persusa...
Behavioural Intervention Ontology (2)
www.sti-innsbruck.at 12
An excerpt of Behavioural Intervention Ontology
• Knowledge sharing in a heterogenous system
• Integration of various data sources
• Inferring behavioural patterns with a...
• Our contribution:
– Providing a holistic system aiming the change in energy
consumption behaviour of individuals
– Two s...
Thank you for your attention
www.sti-innsbruck.at 15
«No to the spy in our household»
https://www.facebook.com/entropyproj...
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Umutcan Şimşek, Anna Fensel, Anastasios Zafeiropoulos, Eleni Fotopoulou, Paris Liapis, Thanassis Bouras, Fernando Terroso Saenz and Antonio F. Skarmeta Gómez | A semantic approach towards implementing energy efficient lifestyles through behavioural change

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Umutcan Şimşek, Anna Fensel, Anastasios Zafeiropoulos, Eleni Fotopoulou, Paris Liapis, Thanassis Bouras, Fernando Terroso Saenz and Antonio F. Skarmeta Gómez | A semantic approach towards implementing energy efficient lifestyles through behavioural change

  1. 1. CLICK TO EDIT MASTER TITLE STYLE A semantic approach towards implementing energy efficient lifestyles through behavioural change SEMANTICS’16, Leipzig/Germany Umutcan Şimşek, Anna Fensel, Anastasios Zafeiropoulos, Eleni Fotopoulou, Paris Liapis, Thanassis Bouras, Fernando Terroso Saenz, Antonio F. Skarmeta Gòmez © Copyright 2016 | www.sti-innsbruck.at 14.09.2016 http://entropy-project.eu
  2. 2. Outline • ENTROPY Project • Introduction • Related Work • Motivation • Reference Architecture • Semantic Models – IoT-Energy Monitoring Ontology – Behavioural Intervention Ontology • Conclusions and Future Work 1www.sti-innsbruck.at 1
  3. 3. • The project aims to design and deploy an innovative IT ecosystem targeting at improving energy efficiency through consumer’s understanding, engagement and behavioural change • 3-year project, started in September 2015 • 9 consortium members, including 3 pilots – Pilots: Navacchio Technology Park, University of Murcia Campus, Technopole in Sierre ENTROPY Project www.sti-innsbruck.at 2
  4. 4. • Buildings are responsible of 41% of total energy consumption in Europe in 2010, followed by transport (32%), and industry (25%) [1] • A study conducted in several developing countries [2] shows that providing timely interventions adaptive to user’s behaviour create significant impact on energy saving • ENTROPY platform consolidates Internet of Things and semantic technologies in a pervasive system, in order to provide timely interventions through personalized applications and serious games Introduction www.sti-innsbruck.at 3 [1] B. Lapillonne, C. Sebi, K. Pollier, and N. Mairet. Energy Efficiency Trends in Buildings in EU: Lessons from the ODYSEE-MURE Project. Technical report, 2012. [2] A. Pegels, A. Figueroa, and B. Never. The Human Factor in Energy Efficiency. Technical report, German Development Institute, 2015.
  5. 5. • SEMANCO [3] – An ontology-based energy information system for enabling stakeholders to make guided decisions on how to reduce CO2 emissions in cities. • OPTIMUS [4] – Aims to optimize the energy consumption in public buildings through an assesment framework to guide public administrators to create more energy efficient cities • OpenFridge [5] – An IoT data-infrastructure that explores the potential of opening and linking refrigerator energy consumption data for providing services to user communities Related Work www.sti-innsbruck.at 4 [3] L. Madrazo, A. Sicilia, and G. Gamboa. SEMANCO: Semantic Tools for Carbon Reduction in Urban Planning. In Proceedings of the 9th European Conference on Product and Process Modelling, Reykjavik, 2012. [4] A. Sicilia, L. Madrazo, and G. Costa. Building a semantic-based decision support system to optimize the energy use in public buildings: the OPTIMUS project. Sustainable Places 2015, page 101, 201 [5] S. D. K. Tomic and A. Fensel. Openfridge: A platform for data economy for energy eciency data. In 2013 IEEE International Conference on Big Data, pages 43-47. IEEE, 2013.
  6. 6. • Current literature accommodates an abundance of applications that facilitate semantic technologies • However, they mostly focus on infrastructural aspects of energy efficiency domain and target policy makers • We address individuals' energy consumption characteristics and use the infrastructural elements such as sensor and smart meter measurements as a supporting factor Motivation www.sti-innsbruck.at 5
  7. 7. • Fernando is an ENTROPY user • In a summer day, he comes to his office and runs the air-conditioning • Based on the data collected from the weather station, the outside temperature will be lower than the day before • Based on his behavioural analysis, we show him a task via his mobile phone, to open the window and turn off the air-conditioning Motivation: University of Murcia Use Case www.sti-innsbruck.at 6 ¡Hola! Me llamo Fernando. Fernando’s Office
  8. 8. Reference Architecture www.sti-innsbruck.at 7
  9. 9. • IoT-Energy Monitoring Ontology – An ontology to represent energy infrastructure of buildings and sensor observations as well as energy consumption parameters – 59 classes, 6 properties • Behavioural Intervention Ontology – An ontology to represent behavioural interventions – 32 classes, 17 properties • Both ontologies can be found at: http://vocab.sti2.at/entropy ENTROPY Semantic Models www.sti-innsbruck.at 8
  10. 10. • IoT-Energy Semantic Model mainly borrows concepts from the following ontologies – Smart Appliances REFerence (SAREF) [6] • To represent building spaces, building objects and devices – Semantic Sensor Network (SSN) [7] • To represent sensors and observation values – Friend of a Friend (FOAF) [8] • To represent the agents that are active in the building – Linked Data Analytics (LDAO) [9] • To represent the analytic processes applied on certain observation values IoT-Energy Semantic Model (1) www.sti-innsbruck.at 9 [6] http://ontology.tno.nl/saref/ [7] https://www.w3.org/2005/Incubator/ssn/ssnx/ssn [8] http://xmlns.com/foaf/spec/ [9] http://linda.epu.ntua.gr/vocabulary/2290/linked-data-analytics-ontology/
  11. 11. IoT-Energy Ontology (2) www.sti-innsbruck.at 10 An excerpt of IoT-Energy Ontology
  12. 12. • Core concepts of the Behavioural Intervention Ontology – An Intervention in different forms (e.g. list of tasks, persusasive message, gamified quiz) – An Agent that is the target of an Intervention – A Feedback given by a Person to a certain Intervention • Reused ontologies – Friend of a Friend (FOAF) • To represent the agents that are using the platform – mIO! Ontology Network [10] • To represent mobile devices that can be used for identification of user and user’s context – Weighted Interests [11] • To represent people’s preferences regarding energy consumption and efficiency Behavioural Intervention Ontology (1) www.sti-innsbruck.at 11 [10] http://mayor2.dia.fi.upm.es/oeg-upm/index.php/en/ontologies/82-mio-ontologies/ [11] http://smiy.sourceforge.net/wi/spec/weightedinterests.html
  13. 13. Behavioural Intervention Ontology (2) www.sti-innsbruck.at 12 An excerpt of Behavioural Intervention Ontology
  14. 14. • Knowledge sharing in a heterogenous system • Integration of various data sources • Inferring behavioural patterns with a semantic rule based approach • Both models and impact of different intervention techniques will be validated with an initial implementation of reference architecture in our pilots • Further examination of Behavioural Intervention Ontology in different domains (e.g. Marketing, health) • Extension of Behavioural Intervention Ontology with different intervention types Current and Future Work www.sti-innsbruck.at 13
  15. 15. • Our contribution: – Providing a holistic system aiming the change in energy consumption behaviour of individuals – Two semantic models: • A behavioural intervention ontology to represent interventions aiming behavioural change • Alignment and extension of existing IoT and Energy related ontologies in IoT-Energy Monitoring Ontology • We aim to go beyond infrastructure oriented methods and develop technology for achieving energy efficiency by changing energy consumption behaviour. Conclusions www.sti-innsbruck.at 14
  16. 16. Thank you for your attention www.sti-innsbruck.at 15 «No to the spy in our household» https://www.facebook.com/entropyproject https://twitter.com/entropyeu

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