Slides for my talk at AIUkraine2015. Overview of the drivers in the EE industry, products we sell at Opower using Machine Learning and what are consequences of that.

1. Intro v.0.1.0
Cut on burning your
dinosaurs, dude I

2. About me
● 7+ years in IT
● 2 years with Opower
● ~1 year with ML
● 4 mo UMKA climate
change course
● Mosquito assasinator 80lvl

9. Summer’15 in Kiev
● avg. temp +2.9C=21.6C
● precipitation -60%

10. Harkiv

17. What about Nuclear?

18. What about Nuclear?

19. We need 5x more to reduce CO2
emissions 20%

21. Alternative way: Cut consumption

23. Do you know your monthly usage?

24. AMI: Advanced Metering Infra

25. How all of that data is collected

26. How all of that data is collected
<25% :(

27. ML application: Disaggregation

28. Peak time

29. Peak time
Night time
Day time
Peak, e.g. hot weather

30. Capacity is ~const
Nuclear powerplant capacity

31. Peak time is like 10x expensive
Coal generation

32. BDR is our product using ML
● We communicate to selected customers

34. Regulators ask for efficiency

36. EE is our product using ML

40. Results

42. x40 Years

43. Ivan Sobolev
● https://facebook.com/soboleiv
● https://swiftbricks.com