Machine Learning and Simulations for Asset Management at Alliander

Transcript

1. Machine Learning and Simulations for Asset Management March 13, 2020,

   NL-RSE Meetup Gijs Molenaar

2. pythonic.nl • Me! • Freelance Software/Research Engineer • MSc AI

   (UvA) • PhD astronomy (South Africa)

3. • Public Utility company • 5.7 million client connections •

   5700 FTE
4. None
5. None

6. The problem • Fixed budget • How to optimally distribute

   budget over departments • Balance between long term and short term goals

7. Amsterdam • Go fully electric by 2030 • 900.000 people

   • 230.000 private cars • 10 years • 2610 working days • 1000 charging stations a day?

8. Our solution • Build agent based simulation of company •

   Run millions of simulations up to 40 years in the future • Let Reinforcement Learning agent explore and optimize problem space

9. What do we need? • A physical model • A

   bunch of agents manipulating this model
10. None
11. None

12. Reinforcement Learning

13. Reinforcement Learning st st+1 a

14. Deep-Q learning Simulation agent != Reinforcent learning Agent

15. State • Number of connection issues Reward • function of

    • Budget spend • Average waiting time for new connection • Average Waiting time for fix of broken connection Action Decision of budget split

16. Project structure • 3 teams: • Data Engineers • Data

    Scientists • Software Engineers
17. None

18. Evaluating 2 strategies • Compute in neo4j • Compute in

    NumPy + NetworkIt
19. None

20. Rpy2 R has NA values in int arrays, numpy has

    NaN in float arrays

21. Status report • Still working on simulations • Underestimated the

    effort required to get this right • Maybe too ambitious? • Useful nonetheless

22. Research Engineer in Corporate environment • Different setting • Requires

    different mindset • More budget • A lot of politics

23. pythonic.nl available again! • 1 June 2020 • First getting

    married in Namibia • If we don’t get Corona banned

24. Questions?