Data Science Anwendungen bei innogy Netz & Infrastruktur (G&I)

Transkript

1 Data Science Anwendungen bei innogy Netz & Infrastruktur (G&I) - Grid Research and Insights Platform (GRIP) - Essener Energiegespräche 25. Oktober 2018 Philipp Clasen, Freier Mitarbeiter innogy SE

2 1 Einleitung Data Science bei innogy G&I 2 Data Science Terminology: Beyond the buzzwords 3 Use Case 110 kv Transformers: Predictive maintenance 4 Use Case e-prognosis: Prognose von Ladesäulen 5 Use Case Stromausfall.de: Web analytics 6 Fazit

3 EINLEITUNG Data Science bei innogy G&I

4 GRIP Grid Research and Insights Platform Continuous exchange with partners Explore possible benefits Build algorithm DSO 1 DSO 4 DSO 3 DSO 2 problem problem problem problem problem use case use case use case use case use case use case use case Preparation for business Go live use case use case product 1 product 2 product m DSO n problem 4

5 Lighthouse GRIP: Overview of Project Structure GRIP Lead & Coordination Team 1 Use Case 1 110kV transformers: N.N. (Westnetz) Use Case 2 Stromausfall.de: N.N. (all) Use Case 3 use case name N.N. (CoC Gas) Pool of functional support Agile Coaching 2 UC configuration technical expert = use case lead data scientist data expert controller Use Case 4 E-Mobility: N.N. (Westnetz) external Use Case n asset xyz use case name N.N. (customer) Business Modelling 3 Data Science, IT know how 4 Tasks of teams: 1. Organization and coordination of Use Cases, stakeholder management, budget, quality management 2. Support in agile working methods 3. Buildup of business models, strategic junction of use case value and company s strategy 4. Provision of additional know how (if necessary) like data scientists, data experts, IT know how 5

6 DATA SCIENCE TERMINOLOGY Beyond the buzzwords

7 Artificial Intelligence (AI) AI Machine learning representation learning Deep Learning The true challenge to AI proved to be solving the tasks that are easy for people to perform but hard for people to describe formally problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images Source: Deep Learning (Goodfellow et al )

8 Learning Tasks Machine Learning = learn a task from experience Task Performance measure Experience z.b. handschriftliche Wörter aus Bildern erkennen z.b. % richtig erkannter Wörter z.b. Datenbank mit Bildern handschriftlicher Wörter (und Beschriftung) Source: Machine Learning (T. Mitchell ) 8

9 Kleiner Exkurs in die Botanik Iris Versicolor Iris Setosa Iris Virginica Source:

10 Machine Learning A Simplified Overview Supervised Learning: Learning a Function, e.g. Regression Classification Petal-length 2.45 >2.45 Setosa Petal-width 1.75 >1.75 Versicolor Virginica Unsupervised Learning: Finding Patterns, e.g. Clustering Dimensionality Reduction 10

11 Supervised Learning Instances 1 n Petal/Sepal length/width Features 1 p setosa virginica Label/Target learn a function f X Y Prediction f 2.11,, 1.8 = setosa 11

12 How to Learn a Function Data + Inductive Bias (assumptions) Hard Bias e.g. only consider linear functions Soft Bias e.g. prefer small coefficients (i.e. less complex functions) Complexity # of explanations fitting the data the rest is just math (optimization/search) 12

13 Avoid Overfitting

14 USE CASE 110KV TRANSFORMERS Predictive maintenance

15 110 kv Transformatoren sind robust doch Wartung und Austausch sind kostspielig innogy SE September 2018 source: innogy / Westnetz 15

16 Vorhersage des (zukünftigen) Zustands eines Trafos Classification Problem Sample ID C2H2 CO2/CO Actual TrA_measurement TrA_measurement TrX_measurementY Training set Test set learn a function and test its performance Index Features Label 0 = not critical 1 = critical 16

17 Testing the Function Make predictions on the test set C2H2 CO2/CO Prediction Actual f x i,1,, x i,p test set with 1500 instances 75 critical 1425 not critical The trained model (function) classified 99% of the instances in the test set correctly. Is that good or bad? 17

18 Cost Sensitive Classification Predictions for the test set Prediction Actual Choose a threshold e.g. 0.3 Confusion Matrix Predict. test set with 1500 instances 75 critical 1425 not critical Actual

19 USE CASE E-PROGNOSIS Prognose von Ladesäulen

20 Funktionsweise E-Mobility-Prognose für die Netzplanung Sie erhalten die Anzahl an privaten, gewerblichen, halb-öffentlichen und öffentlichen Ladepunkten sowie die Haupttreiber 100 mal 100 Meter genau Privat: Gewerbe: Haupttreiber: Halböffentlich: Haupttreiber: Öffentlich: Haupttreiber: 2 Ladepunkte 2 Ladepunkte Verkehr und Lagerei 4 Ladepunkte Parkhaus 0 Ladepunkte innogy SE Sparte Netz & Infrastruktur Bereich Netzservice Stromnetze planen mit e-prognosis 20

21 4. Methodik zur Ladepunktprognose Beispielergebnis e-prognosis in Essen Haarzopf Bio-Supermarkt Aldi Discounter Hohe Neuwagenkaufkraft & Einfamilienhäuser Edeka Supermarkt Fuhrpark Höhere Neuwagenkaufkraft & Einfamilienhäuser Elektro-Montagen innogy SE Name TT Monat JJJJ 21

22 USE CASE STROMAUSFALL.DE Web Analytics

23 Stromausfall in Rüttenscheid 23

24 Karteneinträge auf 24

25 Datenvisualisierung für innogy SE Factbook

26 FAZIT

27 Fazit Data Science ist das, was funktioniert, um einen Mehrwert aus Daten zu erzeugen Machine Learning ist ein essentieller Teil von Data Science Daten und ein Data Scientist alleine lösen meistens nicht das Problem 27

28 FRAGEN