1. Big Data als innovatie PDMA Masterclass Big data @CISCO - Amsterdam Jurjen Helmus University of Applied Sciences Amsterdam Innoveren met Big data OF

2. 2 @JRHelmus / Father & partner/ Fiat X1/9 Innovator / Lateral thinker / e-mobility big data researcher Charge volume Charge point address Connection time RFID

3. First remark Deze presentatie is deels gebaseerd op onderstaand artikel, verkrijgbaar voor leden op de PDMA.nl website

4. Second remark balans te zoeken tussen complexiteit en for dummies Geef dus vooral aan als iets te eenvoudig/bekend is

5. 5 Part 1: Wat is big data NIET

6. (op en vraag die niet gesteld is) Big Data is niet het antwoord

7. Big Data is niet direct innovatie (maar kan er wel toe leiden)

8. Big Data is niet één specifieke nieuwe methode (maar een paraplu)

9. Big Data is in essentie niet nieuw (maar een samen gang van 4 werelden) 1.Data Generatie – Sensoren, web2.0, machine data, 2.Data opslag en werking – naast SQL ook NOSQL (non-structured) data opslag, streaming data, meta data 3.Data analyse – sneller, complexer, beter maar vooral machine learning (80’s) en deep learning 4.Data visualisatie – sneller, flexibeler, intuïtiever Data Generatie Opslag en verwerking Statistische analyse visualisatie Bron: Gartner.com

10. 10 Part 2: Wat mag je verwachten van Big Data analytics?

11. 11 Complex eenvoudig Realtime Past time Business intelligence Proces monitoring Big Data analytics Data mining Technologische ontwikkelingen hebben voor een verschuiving naar realtime analyses gezorgd.

12. Big data analytics verloopt volgens een duidelijke methodologie Bron:IDO-LAAD RAAKPRO voorstel & Gartner

13. Big data analytics verloopt volgens een duidelijke methodologie Bron:IDO-LAAD RAAKPRO voorstel & Gartner

14. 6 typische analyses in relatie tot Big Data Cluster analyseClassificationRegression Sentiment analyseAssociation rule learningNeuraal netwerk Visualisatie van statistische technieken Cluster analyse Classification analysis Regressie Sentiment analyseAssociation Rule learningNeural network Tan, P-N, Steinbach, M. and Kumar, V. (2005), Introduciton to Data Mining, Pearson Eduction, Boston, MA

15. Met name machine learning algoritmes zijn sterk ontwikkeld onder invloed van enorme datasets Illustratie van deep learning algoritme deeplearning.stanford.edu/

16. 16 Part 3: Big data als innovatie

17. Data analyse guus

18. 18 Part 4: Innoveren met big data

19. In het traditionele stage gate model is data niet expliciet ingebouwd Traditionele stage gate model

20. Data driven innovation kan innovatie versnellen en output verhogen Data driven stage gate model Analysis Transaction clustering Consumer sensitivity Consumer behavior sementation Consumer purchasing prediction Data testing Agent based consumer Simulation Data based conjoint analysis Consumer purchasing prediction Transaction clustering Evolutionairy computation Bron:Kusiak & Tang, 2006

21. 21 Part 5: Klant gedrag

22. Our dataset consists of >715,000 charge sessions from charge point operators in 4 largest cities Parameter ExampleExplanation Charge point address Admiralengracht 44 Adress of the charge point Charge point operator NuonOwner of the charge point Charging service provider EssentOwner of the used charging card Charge point city Amsterdam Charge point postal code 1057EWZIP code of the area of the charge point Volume 0,86Charged energy [kWh] Connection time 0:14:23Time the car was connected Start Date 18-04-2012Date the session started End Date 18-04-2012Date the session ended Start Time 23:20:55Time the session started End Time 23:35:18Time the session ended Charging time 0:14:23Time the car is actually charging RFID 60DF4D78RFID code of a charging card Charge volume Charge point address Connection time RFID The data is enriched with information from the municipality and Dutch Statistics Agency (CBS) such as parking zones, neighborhoods, demographic & social information

23. Meetbaar maken van klantgedrag Charge point addressRFID Individuele klant Meso niveau Macro niveau Sociale interactie Moment gebonden gedrag Recurring pattern Sociale interactie Connection date time Eigenschappen EV EV Range Max capaciteit Lerend vermogen Laad snelheid Laad patroon infrastructuur Transitie moment unsteady naar steady state Patroon relatie Andere gebuikers aankomstpatroon Time ratio honkvastheid loyaliteit Connection date time weersgevoeligheid slijtage wachtrijen Lokale dynamiek honkvastheid

24. Klantgedrag kan wiskundig beschreven worden waaruit middels cluster analyse klantgroepen ontstaan Sign Explanation Start time Mean and standard deviation of the start time of first charge session of the pattern. This is measured at the left side of the pattern, see Figure 5. End time Mean and standard deviation of the end time of last charge session of the pattern. This is measured at the right side of the pattern see Figure 5. Duration Mean and standard deviation of the connection time. TBS weekdays Mean and standard deviation of time between two charge sessions during weekdays. TBS weekends Mean and standard deviation of time between two charge sessions during weekdays. kWh Two types of parameters are taken into account. The mean and deviation of the kWh charged; and the mean and standard deviation of kWh charged divided by largest charge session over all charge sessions. The latter discounts the effect of the car type. Charging point volatility Variability of amount of charging points per charge session corrected by available charging points per session. This parameter is used both absolute as well as relative. Absolute is the mean amount of charging points user per charge session. Relative takes into account the relevant available charging points per session for the specific EV user. Time Ratio Mean and standard deviation of the charging time divided by connection time C,L,kWh Correlation between the time between two charge sessions and the amount of charged kWh of the last session. For this parameter 0 is no correlation and 1 is maximum correlation. C,S,TR Correlation between time ratio and start time of last session. For this parameter 0 is no correlation and 1 is maximum correlation. Pattern type Type of pattern as displayed in example figures. The pattern is formed by the percentage of total of connection hours per hour of the day Overzicht meetbaarheid gebruikersgedrag in

25. Laadpatronen worden gebruikt ter segmentering At least six (car independent*) user types could be distinguished from the dataset * Sub categories could be defined after taking PHEV/BEV differences into account ** user is regarded as visitor since all charge sessions occurred during weekends Source: CHIEF database

26. Voorbeeld: Taxi ondernemers blijken een stabiliserend gebruikspatroon te hebben Gemiddelde grootte laadsessie versie standard deviatie en aantal laadsessies op t=T

27. Voorbeeld de time Ratio is a leidende factor for V2X applicatie Note: 1.In Amsterdam the non-smart charging points directly start charging after connection 2.Slack exists only after charging is finished while connection remains 3.To identify max battery capacity the data requires 1 time ratio 100% session and 1 << 100% session The time ratio is defined as the charge time divided by the connection time

28. Time Ratio is a leading factor for V2X applications The time ratio is defined as the charge time divided by the connection time Power delivery Net Charging PV charging Net Charging Power delivery Low time rato High time ratio Slack Note: 1.Sessions with time ratio <<100% are best usable for V2X applications 2.Sessions with time ratio of 100% are not useful for V2X, these mostly occur at car sharing session No slack for power delivery/ postponing or slower charging, low V2X potential Slack for other charging modalities, thus high V2X potential

29. Note: for this graph a subset of the data was used since not all charging times are present in the data Predictive V2X technology based on charging behavior reveals sweet spots for different applications The dispersion in the graph is indicative for the predictability of the time ratio. Time ratio versus kWh charged for Amsterdam Potential sweet spot for peak shaving Potential sweet spot for power delivery High v2x potential Low v2x potential Source: CHIEF database

30. Sweet spots in the Amsterdam Area can be found using complex algorithms Currently available data The avg kWh charged per session to per area per user The potential left kWh at start of charging per The Time ratio in combintion with Clustered user types with same high v2X potential Curently not available data The available PV cells per m2 per household to be connected to the V2x solution needs to be substantial The hourly used kWh of households within a selected area gathered by the smart metering systems And of course The local grid must allow V2X technology to be implemented In order to be a sweet spot for V2X the following requirements should be fulfilled Note: Of course we are more than willing to collaborate with data providers in European projects

31. The avg kWh charged per session per user reveals several potential clusters Map of Amsterdam with avg kWh per user Source: CHIEF database

32. Similar clusters were found for mean potential kWh at start of session Map of Amsterdam with mean potential kWh at start of session per user Source: CHIEF database

33. Local mean time ratio displays a different pattern Map of Amsterdam with mean time ratio per user Low mean time ratios occur at different places than the previous slides display Source: CHIEF database Note: a slightly different dataset was used due required to calculate the time ratio

34. ANY QUESTIONS