Mag. Vikash Kumar, Dr. Anna Fensel SEMANTIC DATA ANALYTICS AS A BASIS FOR ENERGY EFFICIENCY SERVICES

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1 Mag. Vikash Kumar, Dr. Anna Fensel SEMANTIC DATA ANALYTICS AS A BASIS FOR ENERGY EFFICIENCY SERVICES

2 Outline Big data trends changing the ways energy infrastructures operate Semantic technologies in a nutshell - What they are and what they are good for Detailed example of FTW previous work in semantic data processing - Next generation of energy efficient buildings Future directions FTW - 2 -

3 FTW - 3 -

4 FTW - 4 -

5 Semantic Web Technology Evolution Incerased adoption beyond the Web 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. Source: Open Knowledge Foundation

6 From Semantic Web to Semantic World: Challenges Converting large volumes of raw data to smaller volumes of processed data - Streaming, new data acquisition infrastructures - Data modeling, mining, analysis, processing, distribution - Complex event processing (e.g. in-house behaviour identification) Data which is neither free nor open - How to store, discover and link it - How to sell it - How to define and communicate its quality / provenance - How to get the stekeholders in the game, create marketplaces Establishment of radically new B2B and B2C services - Tomorrow, your carton of milk will be on the Internet J. da Silva, referring to Internet of Things But how would the services look like?

7 Semantic data processing typical usage Efficient modeling and information integration - Flexibility in schema (ontology model) definition - Scalable handling of data heterogeneous formats - Data summarization and post-processed representation in semantic formats Efficient dissemination - Providing a semantic e.g. SPARQL query endpoint beyond syntactic - access to the data (without this the data is difficult to access, loose) - Reports generation - Opening the data as linked data Efficient data analytics, reasoning and learning - Prediction (e.g. on possible future QoS issues) - Advanced querying (e.g. who can help this user with this device? ) - Rules and policies design, adaptation, evolution e.g. for automation

8 How does the next generation of energy efficient buildings look like?

9 SESAME and SESAME-S Projects 2 FFG COIN Projects (sesames.ftw.at) SESAME Semantic Smart Metering, Enablers for Energy Efficiency ( ) - Prototype, proof of concepts, feasibility study SESAME-S Services for Energy Efficiency ( ) - setting up usable smart home hardware, a portal and repository - organizing a test installation in real buildings: in a school (Kirchdorf, Austria) and a factory (Chernogolovka, Russia) - developing specialized UIs and designing mobile apps for the school use case Consortium partner network of 6 organizations

10 Data Acquisition Example: Installations in Real Life Buildings 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)

11 Data-Driven Management in the Intelligent Building Over 10 million of real life data triples collected in the semantic repository

12 The School Setup Austria)

13 Light Alert Rule Pseudocode IF?ls is a :LightSensor AND?ls :hasinstantmeasurement?im AND?im : hasmeasurement?ms AND?im : atinstant?instant AND?z = Float(?ms) AND?hour = currenttime in hours AND ((?today = weekday AND (19:00 <?currenttime < 06:00 )) OR (?today = weekend)) AND?z > 50 ) THEN CONCAT(( UseCase-A- +?instant) AS?alertLight) AND CONCAT(( Some unnecessary light device was left ON in the room at +?instant) AS?message)

14 CONSTRUCT {?alertlight :atinstant?instant.?alertlight :alertfromlightdevice?ls.?alertlight :hasalertmessage?message. } WHERE { BIND (now() AS?date) BIND (day(?date) AS?day) BIND (month(?date) AS?month) BIND (year(?date) AS?year) ####################### ## Gaussian algorithm for day of the week ## # Subtract 1 to year if January or February BIND (IF(?month<=2, 1, 0) AS?jf) BIND (?year -?jf AS?adjyear) #century and year BIND (floor(?adjyear/100) as?c) #?c is the century BIND (?adjyear - (?c * 100) as?y) #?y is the year # x2 = (month + 9) % BIND (?month+9 AS?x1) BIND (xsd:integer(?x1 - floor(?x1/12)*12 + 1) AS?x2) # body of formula BIND ((?day + floor((2.6 *?x2) - 0.2) +?y + floor(?y/4) + floor(?c/4) - (2 *?c)) AS?i) BIND (?i - (floor(?i/7) * 7) AS?dayIDx) #?i % 7 # ensure result is positive BIND (xsd:integer(if(?dayidx < 0,?dayIDx + 7,?dayIDx)) AS?dayID) #######################?ls a :LightSensor.?ls :hasinstantmeasurement?im.?im :hasmeasurement?ms.?im :atinstant?instant. BIND (xsd:float(?ms) as?z). BIND (SUBSTR(xsd:string(?instant), 12, 2) as?hour). {If the Light Sensor reading is greater than 50 on a weekend or on a weekday after 7 PM or before 6AM, send light sensor alert} { BIND (IF(?z < "50.0"^^xsd:float, 0, (IF (?dayid > 5, 1, (IF((xsd:integer(?hour) > 19) (xsd:integer(?hour) < 7), 1, 0))))) as?alert).} #create an instant of alert appended with timestamp <- Same light alert rule represented as SPARQL query Semantic rules evolve and are adapted to new settings (e.g. other buildings, changed user bahaviour) automatically based on data analytics. } BIND (IF(?alert = 0, 0, URI(CONCAT(xsd:string(:UseCase-A-), xsd:string(?instant) ))) AS?alertLight). BIND (CONCAT(xsd:string("Some unnecessary light device was left on in this room at "), xsd:string(?instant)) as?message).

15 Alert monitoring app Alerts are activated by rules

16 Energy Market Data Weather Forecast Grid Service Information Appliances Info Open Linked Data Even more relevant data Renewables Info Statistical Weather Data Data Acquisition Public Data Cloud Energy Efficiency Services Private User Storage Energy Companies Portal Agencies Consumer MApp Energy Monitoring and Control System

17 Future Directions Vertical integration using converging networks - standard middleware: opc-ua Infrastructure performance monitoring, optimisation and alert services based on data analytics Semantic Services - Data analytics as a service - Platform as a service - Infrastructure as a service New business models and services addressing multiple sectors: - Such as telco, retail, manufacturing Making money with data FTW

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