A presentation given on Metocean analysis and Machine Learning for improved estimates of energy production in WECs by Aaron Barker at the University of Victoria on the 7th of December 2017

1. Presentation Title:
Metocean analysis and Machine Learning for
improved estimates of energy production in WECs
Presenter Name:
Aaron Barker
Metocean analysis and Machine Learning for
Improved estimates of energy production in
WECs
Thursday, December 7th, 2017
University of Victoria

2. MaREI centre, Beaufort Building, Cork, Ireland

4. Oregon State University Campus, Oregon, USA

5. FOUNDING:
In 1945, Senator J. William Fulbright
introduced a bill in the United States
Congress that called for the use of surplus
war property to fund the “promotion of
international good will through the
exchange of students in the fields of
education, culture, and science.” and with
the goal of “mutual understanding between
the people of the United States and the

6. 370,000+
Fulbrights awarded
since 1946
800
U.S. Scholar
grants annually
160
Participants
countries
82
Pulitzer Prize
recipients
58
Nobel
Laureates
33
Heads of State or
Government
31
MacArthur
Fellows
16
Presidential Medal of
Freedom recipients

7. Overview
Context / Goal
Metocean Analysis and Study Site
Wave Model
Tank Testing
Machine Learning

8. Increased activity in the Offshore Renewable Energy Sector has
driven the need for improved understanding of design wave
conditions and wave climate.
To improve the estimation of energy production generated from
scaled wave energy device tests and wave energy resource
assessments.
OBJECTIVE:
MOTIVATION:

9. Killard Point, County Clare.
WestWave Project.
Plan for 25MW installed capacity
WestWave site picture courtesy of ESBI
Study Site

10. Energy Prediction
•Wave Resource energy asssessment from long term dataset - Satelite, WW3 model etc.
•P=1/2 x Hs ^2 x T - Assess yearly power and seasonal variability.
Deployment of measurement devices
•Deployment of Wave Buoys/ADCP to verify wave resource.
•Data retrieval and Processing .
Numerical Modelling
•Numerical modelling of wave resource in the AOI using MIKE21 SW.
•Extend Model to longer-duration hindcast using WW3 input.
Calibration of Numerical Model
•Use Recorded data for calibration of Numerical Model.
•Assess visual and statistical fit of model.
Machine Learning Adjustment
•Use kNN k-crossfold validation with holdout to build predictive model for wave period.
•Conversion from model to recorded.
Full Meteocean Analysis
•Analysis of HS, TP, Seasonal variability, Weather Windows, Extreme Conditions.
•Energy Prediciton using device power matrix.
Site assessment Hierarchy for Killard Point Site

11. Wave Model Setup
Infomar Bathymetry Survey of Clare
region (INFOMAR, 2013)
Mesh Generation in MIKE21

12. Wave Model Setup
Interpolated and smoothed meshTriangular Mesh showing stepped
resolution selection.

13. Wave Resource Output from Killard Point Model
Histogram of Hm0 distribution for Nearshore model 24 year hindcast.
Histogram of Hm0 distribution for Offshore model 24 year hindcast.

14. Wave Resource Output from Killard Point Model

15. Model Validation: Results
Deployment Location
1 2 3
Hs
Bias (%) 10.84 15.48 13.33
RMSE (%) 14.19 19.76 18.59
Rcorr 0.92 0.91 0.95
Tp
Bias (%) 8.09 13.38 8.66
RMSE (%) 10.90 18.03 12.23
Rcorr 0.74 0.73 0.79
Te
Bias (%) 6.20 9.71 6.83
RMSE (%) 7.96 12.85 8.81
Rcorr 0.86 0.82 0.87
Power (J)
Bias (%) 22.68 10.30 26.38
RMSE (%) 34.36 13.26 56.57
Rcorr 0.91 0.90 0.93
Statistical analysis of MIKE 21 SW model using MeteoGroup input against buoy
deployment data.

16. Spectral analysis of buoy data
• Perform spectral analysis of time-series data from buoy to determine spectral wave parameters including Hs,
Tp, Te.
• Visualise the distribution of spectral energy.
Selection of Sea State for study
• Sea-State Represented by Hs,Tp and plotted at 0.5m Hs and 1s Tp bins as per IEC standards.
• Identify the most important sea states based on occurrence and relative energy contribution.
Identification of representative time-series
• Plot the wave spectral density of each time-series record.
• Identify the time-series which produces the closest to the "average" spectrum for this bin and record time-
series data alongside Hs, Tp.
Generate equivalent Bretschneider spectrum
• Generate a bretschneider spectrum and compare to the spectral shape of the time-series record.
• Generate a time-series representation of the Bretschneider spectrum using the Njord wave synthesis
software.
Test Bretschneider and Time-series in tank
• Perform model testing to determine the difference in power production between Bretschneider and the
distribution at Killard Point.
Estimate Energy production using new dataset
• Using pressure sensor and elevation records from tank testing, compare energy production for Bretschneider
and Real Spectrum.

17. 1) 2304 x 30 minute time-series of Hm0 and
T02.
2) Spectra generated from each 30
minute timeseries. AssociatedHm0 and
T02 values determined for each.
3) Spectra “binned” according to Hm0
and T02 values into X x Y matrix. Bin here
shows the average T02 parameter.
4) Multiple Spectra(grey) overlaid and
average spectrum generated (Blue
curve).
5) Bretschneider Spectrumgenerated
using Hs and Tp of averagespectrum.
Spectral Analysis and
Partitioning

18. Variation Across Sea-States
Individual (grey), Average (blue) and Bretschneider Representation (red) for 4 of the most commonly
occurring sea-states at Killard Point

19. Spectra
RAO
Heave Spectral Amplitude
Spectra
Energy Production estimate from NEMOH model cylinder point absorber

21. Altering Empirical Spectral Shapes
𝑆 𝜔 =
5
16
𝜔 𝑚
4
𝜔5
𝐻1/3
2
𝑒−5𝜔 𝑚
4 /4𝜔4
𝑆 𝑓 = 𝐴𝑓−5exp(−𝐵𝑓−4)
𝐵 =
0.751
𝑇𝑎𝑣
4
𝐴 = 𝐵 × 𝐻 𝑚0
4

22. Altering Empirical Spectral Shapes
𝐵 =
0.686
𝑇𝑎𝑣
4

24. Energy Production Using Power Matrix for AW Energy Waveroller Device

25. Tank Testing of OWC devices to determine spectral shape variation impact on
energy production

26. Deep Ocean Basin at MaREI Devices in action

27. Transitional
regime
Phase
differenc
e
Tank Testing results

28. Comparison of power output: Recorded vs Bretschneider

29. DEVICE POWER PRODUCTION (WATT HOURS & PERCENTAGE DIFFERENCE) FOR REAL AND BRETSCHNEIDER WAVES

30. Need for machine Learning correction
•Model does not correctly represent Wave Period Conditions.
•This affects device power production estimates.
Model and Buoy Data differences
•Wave Period from Numerical data ~15% greater on average. Relationship is dependant on wave
period and wave height as well as the site characteristics.
Setup Approach
•Building Model with Wave Height and Period from both Numerical and Recorded datasets.
•Identify Hs and T01 as predictors of Recorded period.
Machine learning Model
•Weighted kNN model using inverse distances and k-fold cross-validation training approach.
•Holdout and testing split of dataset multiple times, minimizing error of prediction for training
set.
•Prediction on test set and measurement of diffrence between prediction and recorded values.
Build and Apply Model
•Use the model parameters with the lowest prediction error based on repeated testing.
Incorporate OLS prediction, Lasso, Ridge models if error determined to be lower than kNN.
•Create final model and estimate recorded wave period for entire dataset.
Estimate Energy production using new dataset
•Using new dataset, compare energy production for estimate of recorded data and recorded
data.
•Assess model performance.
Machine Learning Methodology

31. Model
Parameter Selection for Models at Belmullet
T1 Hs, T1 Hs, T1, Tp Hs, T1, Tp, Dp
Machine
Learning (ML)
0.13006 0.13037 0.06341 0.06385
Constant
Wave
Period Ratio
(CWPR)
0.15194 0.15107 0.15300 0.15089
Bretschneider
(BRET)
0.31557 0.31424 0.31701 0.31453
Parameter Selection for Machine Learning Model

32. Model
Bias MSE
Training Set Test Set Training Set Test Set
ML -0.0197 -0.0076 0.0293 0.0296
CWPR -0.0621 0.0101 0.2372 0.2371
BRET 0.5112 0.5352 0.4951 0.4929
Model Training Time

33. Characterising seasonal impact on Wave Period Ratio and Learning Times
Model Seasonal LOOCV Effect
Winter Spring Summer Autumn
ML -0.36555 -0.72630 -0.12716 -0.15632
CWPR 1.24945 1.11828 1.25057 1.25255
BRET 1.55446 1.55446 1.55446 1.55446
Model Hm0 RMSE (m)

34. Model MSE RMSE Bias MAE
Max
Error
Rcorr
ML 0.0286 0.1691 -0.0015 0.1185 1.2275 0.996
CWPR 0.2339 0.4836 -0.0553 0.3536 3.2039 0.9756
BRET 0.4922 0.7016 0.553 0.557 3.8025 0.9756
Model
Power
Production
(kWh)
Power
Production Error
(kWh)
Power Production
Error (%)
ML 1.3519 x105 -4.8260 x103 -3.45%
CWPR 1.2950 x105 -1.0520 x104 -7.51%
BRET 1.8270 x105 4.2709 x104 30.50%
Machine Learning Model
Performance at Killard Point

35. • Progress from estimating WPRs
– Consider the full spectral shape
– Provide spectral information necessary to fulfil modelling requirements
• Determine optimal strategy to “re-create” spectral energy profile
– Robust and coherent methodology
– Recommendations and rules to carry out precise wave resource characterisation.
• Train Machine Learning Model
– At West Coast sites in both Ireland and the USA.
Next Phase of Machine Learning Development

36. Next Phase of Machine Learning Development
(Hs,Tp)
(2.15,10)
Train Model using (Hs,Tp) pairs
and full spectral
representation

37. Next Phase of Machine Learning Development
Weight each frequency ordinate according to model
learning
Wattana Kanbua et. al
Build matrix of spectral weights for
each Hs, Tp pair
Train model at Killard Point,
Belmullet & PMEC-SETS/NETS

38. • Spectral Shape has a very large impact on power production estimates
• Standards (Such as the IEC-TS) will need to address this variation, and how it can
be dealt with in each step from conceptualisation to full-scale testing
• Development of Machine Learning Model has the potential to vastly improve our
estimates of energy production
Conclusions

39. THANK YOU
https://ie.linkedin.com/in/aaronthomasbarker