We use machine learning with advanced neural networks to accurately forecast production. There is big data processing, a data hub that we built ourselves and is actually quite cool, and even a data lake for complete buzzwordyness. And once we have the forecast, we start and stop the machines to optimise production. Hard problems = good times!

1. Running windmills with
machine learning
Anette Bergo, Data Science Lead at Bouvet Bergen
@anettebgo

2. ”To accurately forecast
and control power
production from wind
parks, in order to
optimize profit.

3. Trond S. J.
Data Scientist
Eirik
Architect Trond T.
Developer
Anette
Prosjektleder

4. Some basics

5. How windmills work
o Wind turbines convert kinetic
energy in the wind into
mechanical power.
o Generators in the turbine
nacelle converts this mechanical
power into electricity

6. How production works
!" ⁄$ %
&'ℎ
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

7. o Wind farms must nominate expected power output to the
grid operator (ENTSOE) the day before
o Operator asks for more or less until expected consumption
has been nominated.
o Operator can order shut-down (curtailment)
• Too much power in the grid = busted electronics
• Not enough power in grid = brownouts/blackouts
How the market works - nominations

8. o Production is priced per 15 minute unit
o … price fluctuates constantly (think stock market)
o ... the maximum deviation from 0 within the time unit is the
price for the whole unit
• 11:00 125 Euro/MWh.
• 11:08 140 Euro/MWh.
• 11:12 -141 Euro/MWh.
Imbalance market

9. How
weather
works

10. Nominal power curve
+ weather forecast
= Simple and imprecise
production forecast

11. Weather
forecast
Production
curve
Market
behaviour
Model and improve

12. Weather forecast
Climatology

13. Numerical weather
predictions
o Weather forecasts are generated by
numerical weather prediction models
o NWP models uses mathematical models
of the atmosphere and oceans to
predict the weather based on weather
data assimilated at model run time
o NWP models are solved numerically
using finite difference schemes in a
predefined grid.

14. Model accuracy

15. Inaccuracies
o Imperfect modelling of the atmosphere
o Imperfect parameterization of topology
o Imperfect data assimilation (now-
weather)
o Sub-grid scale weather phenomena
(ultralocal)
o Temporal resolution (time)

16. Correcting the NWP:
Static conditions
o Static factors - dirty wind, topology =
systematic bias.
o Simple statistical corrections for
systemic bias
o Site-specific error correction of NWP

17. Correcting the NWP – measured conditions
o Sensors measure actual wind:
• Direction
• Speed
• Gustiness
o Other variable factors – humidity,
cloud or sun etc.
o Sensor data is cleaned using
simple learning algorithms
• Outlier detection
• Identify operational state
• Temporal & grid interpolation /
aggregation

18. NWP
Bias
NWP
Forecast
Observations Post
Processing
Error
Correction
ML
Level
Errors
Phase
Errors
Deep
Learning
Improved
Forecast
Accuracy
Improve Numerical
Weather Prediction
o NWP exhibit systematic biases
o Atmospheric state may play a role
o Covariation between forecast and
observations is often highly non-linear
o More complex learning algorithms learn
from previous production vs previous
forecast vs previous measure

19. Improving the production curve
Learning about the real world

20. O&M
Sensor
Errors
Outages Curtailmet
PhysicsML
Hidden
Markov
Domain
Knowledge
Unsuper-
vised
Improved
Forecast
Accuracy
Operational State
o Domain Knowledge is important
o Multiple models: one for each state
o Physical laws sets some limits
o Unsupervised learning
o Density-based clustering
o Temporal regimes

21. Contextual
Outliers
Point
Outliers
Collective
Outliers
Distribution
Assumption
Robust
StatisticsML
Elliptical
Envelopes
Mahalanobis
Distance
Conditional
KDE
Improved
Forecast
Accuracy
Outlier Detection
o An outlier is generated by a different
statistical process …
o … abnormal distance from other values
o Requires a distributional assumptions
o Robust statistics with high breakpoint
o Conditional quantiles
o Curse of dimensionality
o Dimensionality reduction
o Turbine level: Speed vs. power output
o Cross-turbine: Climatology

22. DirectionVelocity
Observations
Air
Density
Non-
linearityML
Function
Approxi
mation
EPCDeep
Learning
Improved
Forecast
Accuracy
Power Output
o NPC exhibit systematic biases
o Atmospheric state may play a role
o Climatology coverage
o Beware of extrapolation
o Empirical power curves:
• Binning Scheme (reference model)
• Random Forest
• Boosting
o Aggregation to farm level:
• Deep learning (neural nets)

23. Predicting the market

24. o A few easily modelled behaviours – e.g. sharp dip in prices
followed by sharp rise
o More to come?
To come…

25. Improved
Weather
forecast
Empirical
Production
curve
Modeled
Market
behaviour
Precise about production

26. Technical implementation

27. o iPaaS built by Bouvet
o Integrations, data flows, minor
transformations
o Each integration point is a
separate microservice
Sesam

28. In a picture
Data Exchange

29. The animals

30. Birds
Dear all,
To day the young sea eagle has leave the nest.
DTbird on Turbine 28 is unfortunately not yet in place.
We decide to stop T29, T30 and T31 to protect the bird.
Best regards,
Niek

31. o Sometimes, flocks of
bats fly through the
area.
o We are looking at
training models to
predict when.
o And send a signal to
shut down.
Bats
…yes, the Bat Signal.

32. Simple equations
Refined using machine
learning