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A semantic approach towards implementing energy efficient lifestyles through behavioural

changeSEMANTICS’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

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

• The project aims to design and deploy an innovative IT ecosystem targeting at improving energy efficiency through consumer’sunderstanding, 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

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• 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’sbehaviour 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 seriousgames

Introduction

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[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.

• 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

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[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.

• 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

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• 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

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¡Hola! Me llamo Fernando.

Fernando’s Office

Reference Architecture

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• 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

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• 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)

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[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/

IoT-Energy Ontology (2)

www.sti-innsbruck.at 10An excerpt of IoT-Energy Ontology

• 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

Behavioural Intervention Ontology (2)

www.sti-innsbruck.at 12An excerpt of Behavioural Intervention Ontology

• 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

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• 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

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Thank you for your attention

www.sti-innsbruck.at 15«No to the spy in our household»

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