SMART DATA FOR BEHAVIOURAL CHANGE: TOWARDS ENERGY EFFICIENT BUILDINGSAnna FenselSemantic Technology Institute (STI) Innsbruck, University of Innsbruck, AustriaContact: anna.fensel@sti2.at23.03.2017, Lunch Seminar of Institute of Computer Science, University Innsbruck, Austria

Motivation Background Main Story: OpenFridge Extensions / Ongoing further work

- ENTROPY project: linking to psychology- BYTE project and BBDC: linking to sociology- DALICC project: linking to law

Outline

MOTIVATION

From: “The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” – Tim Berners-Lee, James Hendler, Ora Lassila, 2001

Till: Smart Data??

Solved Problem

Going mainstream: schema.org,...

Linked Open Data cloud counts 25 billion triples

Open government initiatives

BBC, Facebook, Google, Yahoo, etc. use semantics

SPARQL becomes W3C recommendation

Life science and other scientific communities use ontologies

RDF, OWL become W3C recommedations

Research field on ontologies and semantics appears

Term „Semantic Web“ has been „seeded“, Scientific American article, Tim Berners-Lee et al.

Semantic Web Evolution in One Slide

2008

2001

2010

2004 Source: Open Knowledge Foundation

Unsolved Problem: Climate Change

Climate Change, Nordkette Innbruck

Climate Change, North of Russia

Computers still depend on humans in energy delivery

Image credit: ClinGroup Holding

Finally – societal impact of (Big-)data…

BACKGROUND

Making Smart Data from Big Data with semantics for energy efficient

buildings - Where it started…

Image credit: kurier.at

In the topic since 2009, with 5 national and EU funded projects

Was present in media such as:

Graduated several PhD, Master and Bachelor students Published and reviewed at high quality energy venues e.g.

Energy Efficiency journal (SCI IF 2015: 1.183), but also at high quality Computer Science venues

Received awards e.g. Highly Commended Paper 2015 of Int. J. of Pervasive Computing and Communications, Best Short Paper Award of iiWAS 2013

Gave invited talks e.g. at Skolkovo, ESTC Was recently proposal evaluator for H2020 ENERGY call

My credentials in the topic of energy efficiency, smart buildings,

responsible use of associated data

Semantic Smart Home Demonstrator – SESAME Project

School in Upper Austria

Factory floor in Russia

Real Smart Building Setups

Smart Building Installations

Motivation: work with real buildings, real data and real users

Technology: Several Smart Meters Sensors (e.g. light, temperature, humidity) Smart plugs, for individual sockets Multi-utility management

(i.e. electricity, heating) Shutdown services for PCs User interfaces and apps: Web, tablet,

smartphone (Android)

Data-Driven Management in the Intelligent Building - SESAME-S Project

- Millions of real life data triples collected in a semantic repository- Ontology published at CKAN

Services Addressing Users @ School Energy awareness,

monitoring

Remote control - manual and programmed - e.g. scheduled activities (ON/OFF policies) and triggering rules (Alert sending rules)

How do we get the users?By having workshops with pupils: introduction to energy efficiency, building analysis, explaining the system and services

+ building administrators

MAIN STORY: OPENFRIDGE

Fensel, A., Tomic, S.D.K., Koller, A. “Contributing to Appliances’ Energy Efficiency with Internet of Things, Smart Data and User Engagement”. Future Generation Computer Systems, Elsevier.

DOI: SCI-indexed journal, 2015 Impact Factor: 2.430; CORE journal rank: A• Fensel, A., Gasser, F., Mayr, C., Ott, L., & Sarigianni, C. (2014).

Selecting 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.

The presentation is based on…

http://dx.doi.org/10.1016/j.future.2016.11.026

Smart Grid is a Showcase for Data Economy

Smart GridOperation Energy Markets

SynchroPhasers

Renewables Parks

Compliance

Smart Buildings

ElectroMobility

Smart Cities

Smart Appliances

SmartMetering

Plant Automation

BusinessDSM

Compliance

Price Signals

Demand Response

Capacity Management

Prosumers

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

What is energy efficiency?– Using less energy to provide

equivalent service.– A life-cycle characteristic of home

appliances.

Economy for Energy Efficiency Data (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-openfridgev2

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

A Value-chain for Energy Efficiency Data

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-openfridgev2

Developing a crowdsourcing platform for data collection

Exploring the concept of context-dependent energy efficiency

Combining (big) data and semantics for add-value services

OpenFridge : Opening and Processing Appliances Data for Energy Efficiency

Improved labeling

Improved technology

and CRM

Better decisions

about replacement

and use Home Users

Labeling Institutions

Manufacturers

Energy Efficiency

Data

Building an ecosystem around data

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

Usage profile avg. consumption, cooling cycle,defrost cycle,…

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 Context to Recommendations

Measurementspower level (5s)timestamp

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

Hardware & service interfaces for data acquisition

- Currently based on the existing commercial system with web-service interface

Big data & analytics for data processing- 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

Platform Enablers

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

Interfaces- 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

Challenges

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

Community & Content Management

Big Data Infrastructure

Data AcquisitionWeb Service

Drupal Portal &Web Service Client

Recommendations &Visualizations

Appliance ProfileMeasurements Profile

Appliance ProfileMeasurements ProfileMeasurements

Business IntelligenceServices

Users

ManufacturersLabeling Organisations

OpenFridge Architecture

Semantic

Knowledge

Base

AnalyticsSPARQL:

Data-as-a-Service

Usage Profile

Volume?Variety?Velocity

?Veracity

?Value?

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

OpenFridge Ontology – Main Classes

Semantic Annotation Process Overview

Tools for Data Fetching

Sources for Fridge Models Data

Results for Data Extraction

Tool: Python• Importation process• Restructure process• Creation of the ontology-file

Result:• OpenFridge ontology published at: http://

www.sti-innsbruck.at/results/ontologies, and indexed at LOV portal

• 1032 refrigerator models with 18665 triples

Data Mapping – Implementation & Results

Technical:● How to design an ontology 100% reusing other schemes● How to fetch data from different HTML Web sources● Screen scraping tools● Creation of readable instances in protege● How to get this data into a format that is readalbe for a tool

like Protege○ How to develop○ Challenges

Organizational:● Managing project (devide tasks)● Meetings (how to communicate)● Engagement

Lessons Learned

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- With test users & external

Implementation Steps

OpenFridge@WFF, Oct 2013

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

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 services

- Querying and reasoning Focusing on refrigerators as they are the largest energy consuming

home appliance; the same principles could be further extended

Summary of the OpenFridge Platform

From general project presentation: http://www.slideshare.net/slotomic/big-data-openfridgev2

Components of the OpenFridge Platform

Overview of High-level Concepts of OpenFridge Ontologies

Visualization of a Single Measurement Data

Visualization of the Aggregated Measurement Data

Aims: To identify whether the users were capable of using

the platform as a whole, and their response rates to it, on the hardware, software and service levels

- between October 2014 and September 2015, the platform acquired 68 active users. Engaged were ca. 100, but for the rest the platform did not work for different reasons (hardware failure, wireless incompatibility, inability to set-up)

To receive the feedback on the system’s existing and potential features, particularly, regarding actual and potential usage of collected data

- Survey at the end: 21 respondents (19 male, 2 female), - 66,7%, came from Austria – the rest from the rest of the world, - 90,5% of responders were running OpenFridge on Windows

operation system, and the rest were split between Linux, Android, iPhone/iPad.

Evaluation and Results

What do you see as the most useful feature(s) and impact of the

OpenFridge portal?

Which data and knowledge engineering issues on the portal have

you experienced?

Would you share the data collected from your appliances, and under

which conditions?

Which features do you think would increase your engagement with the

platform?

An Internet of Things semantic platform OpenFridge is designed and implemented.

The platform has been deployed and evaluated with globally-distributed real-life users.

Real-life user and fridge measurements data has been collected and published in open source repositories.

A set of selected characteristic anonymous fridge and freezer measurements, including the detailed observation data of 426 complete cooling cycles, composition of used ontologies, data of >1032 refrigerator models: in the datahub: http://datahub.io/dataset/the-measurement-data-set-from-the-project-open-fridge, http://www.sti-innsbruck.at/results/ontologies, and indexed at LOV portal

High potential in facilitation of data economy has been demonstrated in evaluations.

Challenges in deployment of such platforms are discussed.

Summary - Highlights

EXTENSIONS / ONGOING FURTHER WORK

- ENTROPY - BYTE, BBDC

- DALICC

• The H2020 ENTROPY project aims to design and deploy an innovative IT ecosystem targeting at improving energy efficiency through consumers understanding, engagement and behavioural changes.

• http://entropy-project.eu • 3 real-life pilots (Italy, Spain, Switzerland)• Energy efficiency facilitation taking into account personality profiles of the

users• Ongoing work

Energy beliefs• 97% believe energy conservation is something to be concerned about• 95% feel that conserving energy and natural resources is important to

them• 94% believe conserving energy also their problem• 91% have responsibility to conserve energy and resources• 90% believe the organization they work for should conserve energy• 92% believe they should and would help organization conserve energy• >85% willing to change daily routine to conserve energy

however…• Only 55% agree their country is in the middle of an energy crisis• 20% feel news reports about energy crisis are blown out of proportion• >70% believe it is their right to use as much energy as they want

Innsbruck - Austria 2-3 February, 2017

Energy behaviour• Almost all turn off the room/bathroom lights when

they leave• 70% turn off computers • >60% turn off the PC monitor• ~50% turn off Air Conditioner(s)• 23% turn off printer(s)• 14% often leave the windows open with Aircon on

Innsbruck - Austria 2-3 February, 2017

Gamification User Types• Using player typologies to understand individual

preferences is one of the common approaches for personalization

• Personalizing gameful systems more effective than one-size-fits-all approaches.

• Several studies indicated the need for personalizing gamified systems to users’ personalities.

• Personalization can be used in gameful design to tailor interaction mechanics to the user.

Innsbruck - Austria 2-3 February, 2017

HEXAD Gamification - User Types 1-3Hexad gamification user types (Tondello et al., 2016):

• Philanthropists – motivated by purpose, altruistic and willing to give without expecting a reward.

• Suggested design elements: collection and trading, gifting, knowledge sharing, and administrative roles.

• Socialisers – motivated by relatedness – want to interact with others and create social connections.

• Suggested design elements: guilds or teams, social networks, social comparison, social competition, and social discovery.

• Free Spirits – motivated by autonomy, freedom to express themselves and act without external control – like to create and explore within a system.

• Suggested design elements: exploratory tasks, nonlinear gameplay, Easter eggs, unlockable content, creativity tools, and customization.

Innsbruck - Austria 2-3 February, 2017

HEXAD Gamification - User Types 3-6• Achievers – motivated by competence – seek to progress within a system by completing tasks, or prove themselves by tackling difficult challenges.

• Suggested design elements: challenges, certificates, learning new skills, quests, levels or progression, and epic challenges (or “boss battles”).

• Players – motivated by extrinsic rewards – will do everything to earn a reward within a system, independently of the type of the activity.

• Suggested design elements: points, rewards or prizes, leaderboards, badges or achievements, virtual economy, and lotteries or games of chance.

• Disruptors – motivated by triggering changes – tend to disrupt the system either directly or through others to force negative or positive changes, test the system’s boundaries and try to push further. Although disruption can be negative (e.g., cheaters or griefers), it can also work towards improving the system.

• Suggested design elements: innovation platforms, voting mechanisms, development tools, anonymity, anarchic gameplay.

Innsbruck - Austria 2-3 February, 2017

Although users are likely to display a principal tendency, in most cases they will also be motivated by all the other types to some degree (Tondello et al., 2016).

Gamification user types• Achiever rated high by 89% of participants.• Philanthropist by 88%• Socializer by 76%• Free Spirit by 75%• Player by 43%• Disruptor by 12%

Innsbruck - Austria 2-3 February, 2017

Correlation of user types & game elements

In addition to gamification user type prefs offered in bibliography, for an energy conservation app, our sample prefer:

• Philanthropists badges and roles.• Socialisers points, badges, rewards and roles.• Free spirits points, badges, progression, status, levels and

roles.• Achievers no specific preference towards any of the elements.• Disruptors status.• Players rewards, points, badges, leaderboards, status

Innsbruck - Austria 2-3 February, 2017

Personality Profile– The big 5 personality traits have been

correlated with:– Pro-environmental attitudes & environmental

engagement– Concern For Privacy in LBS & Usage Intention of

Location-Based Services– Game Playing Style, Behaviour, Motivations to

Play, Difficulty adaptation– Player typologies– Game genre preferences

Innsbruck - Austria 2-3 February, 2017

Engagement• The “positive work-related state of fulfilment that is

characterized by vigor, dedication, and absorption”, the positive antipode of burnout. (Schaufeli, Bakker and Salanova, 2006)

• Gallup’s categorization of employees, based on level of engagement (Prakash and Rao, 2015):

• Engaged: work with passion and feel a profound connection to their organization, drive innovation and move the organization forward

• Non-engaged: are essentially “checked-out”, sleepwalking through their workday, putting time but not energy or passion into their work

• Actively disengaged: are not just unhappy at work, but busy acting out their unhappiness, undermining what their colleagues accomplish, every day

Innsbruck - Austria 2-3 February, 2017

BYTE: The BYTE research roadmap

Anna Fensel and Marti Cuquet, University of Innsbruck, Austria

BYTE final conference, London, UK, 9 February 2017

Big data roadmap and cross-disciplinary community for addressing societal externalities

Starting points: research topics from BDVA and literature survey

• Research topics from BDVA’s Strategic Research and Innovation Agenda.• Defines overall goals, technical and non-technical priorities and a research and innovation

roadmap.

• 6 main priorities:

Data management

Data processing

Dataanalytics

Data protection

Data visualisation

Non-technical priorities

to handle unstructured data, ensure semantic interoperability, asses data quality and provenance

Optimised and efficient architectures for data-at-rest and data-in-motion, decentralised, scalable

with improved models and simulations, semantic analysis, pattern discovery, business intelligence and predictive and prescriptive analytics

and anonymisation to enable not open data enter the Data Value Chain with a complete data protection framework,anonymisation algorithms, multiparty mining

and user experience, with interactive and personalised visualisations, simplified query and discovery mechanisms, linked data visualisations

skills development, standardisation, social perceptions and societal implication.

Data management Data processing Data analytics Data protection Data visualisation Non-technical priorities

A1 Handling unstructured data

B1 Architectures for data-at-rest and data-in-motion

C1 Improved models and simulations

D1 Complete data protection framework

E1 End user visualisation and analytics

F1 Establish and increase trust

A2 Semantic interoperability

B2 Tools for processing real-time heterogeneous data

C2 Semantic analysis D2 Data minimization E2 Dynamic clustering of information

F2 Privacy-by-design

A3 Measuring and assuring data quality

B3 Scalable algorithms and techniques for real-time analytics

C3 Event and pattern discovery

D3 Privacy-preserving mining algorithms

E3 New visualisation for geospatial data

F3 Ethical issues

A4 Data management lifecycle

B4 Decentralised architectures

C4 Multimedia (unstructured) data mining

D4 Robust anonymisation algorithms

E4 Interrelated data and semantics relationships

F4 Develop new business models

A5 Data provenance, control and IPR

B5 Efficient mechanisms for storage and processing

C5 Deep learning techniques for BI, predictive and prescriptive analytics

D5 Protection against reversibility

E5 Qualitative analysis at a high semantic level

F5 Citizen research

A6 Data-as-a-service model and paradigm

C6 Context-aware analytics

D6 Pattern hiding mechanism

E6 Real-time and collaborative 3-D visualisation

F6 Discrimination discovery and prevention

D7 Secure multiparty mining mechanism

E7 Time dimension of big data

E8 Real-time adaptable and interactive visualisation

Process1) Discussion and validation ofresearch topics

•Work in small round tables.• Are the topics representative?• Are there other relevant topics or subtopics?• Are there other relevant sources aside from SRIA you’d like to incorporate?

2) Alignment of research topicsand externalities

• BYTE identified externalities have been grouped in 4 groups and 18 subgroups

3) Time alignment and prioritisation

BYTE Research Roadmap - Heatmaps

BYTE Big Data Research Roadmap - Summary

• Presents positive and negative externalities of big data in 18 industry sectors.• Maps research to its societal impact and contribution to skills and standards.• Provides a timeline for research efforts with its impact on each sector.• Summarises best practices to capture the positive societal benefits of big data.

• Compact version: Cuquet, M., & Fensel, A. (2016). Big data impact on society: a research roadmap for Europe. arXiv preprint arXiv:1610.06766. URI: https://arxiv.org/abs/1610.06766

• Full version as D6.1 BYTE deliverable: http://byte-project.eu/research

• Join BYTE Big Data Community (BBDC): http://new.byte-project.eu/byte-community

Data licensing

Image from DALICC consortium: FH St Pölten, STI Innsbruck, WU Wien, Semantic Web Company, Höhne i. d. Maur & Partner Rechtsanwälte OGhttps://www.dalicc.net/

Data licensing is still complicated, formats for licensed data use are under-defined.Semantic standards for license development are in progress e.g. ODRL, RightsML.Automated semantic-based data licensing support for derivative works is our ongoing work.

Thank you for your attention!Questions?