Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Machine learning approach for optimal determination of wave parameter relationships

© The Institution of Engineering and Technology
Received 30/11/2016, Accepted 21/06/2017, Revised 18/06/2017, Published 22/06/2017

Wave parameter relationships have long been determined using methods that give non-standard and often inaccurate results. With increased commercial activity in the marine sector, the importance of accurate wave parameter relationship determination has become increasingly apparent. The outputs of many numerical models and buoy datasets do not include all requisite wave parameters, and a typical approach is to use a constant conversion factor or relationship based on defined spectra such as the Bretschneider or the joint North Sea wave observation project (JONSWAP) spectrum to determine these parameters. Given that relationships between wave parameters vary significantly over both hourly and seasonal and annual timescales, the currently employed methods are lacking, as subtleties are missed by the simpler approach. This paper addresses the determination of wave parameter relationships using a machine learning (ML)-based model, identifying and selecting the optimal method for the conversion of wave parameters (T e, T 01) in coastal Irish Waters. This approach is then validated at two sites on the West coast of Ireland. The aim is to highlight the utility of ML in approximating the relationship between wave parameters; using both buoy and modelled data, and mapping the predicted outcomes for a wave energy converter based on a variety of ML and measure correlate predict approaches.

Inspec keywords: power engineering computing; wave power generation; ocean waves; learning (artificial intelligence)

Other keywords: Ireland; coastal Irish waters; optimal wave parameter relationship determination; marine sector; machine learning approach; wave energy converter; buoy datasets; wave parameter conversion; Bretschneider spectrum; numerical models; constant conversion factor; JONSWAP spectrum

Subjects: Knowledge engineering techniques; Wave power; Power engineering computing

References

    1. 1)
      • 10. Tibshirani, R.: ‘Regression shrinkage and selection via the Lasso’, J. R. Stat. Soc. B, 1996, 58, (1), pp. 267–288.
    2. 2)
      • 22. Gorman, R.M., Bryan, K.R., Laing, A.K.: ‘Wave hindcast for the New Zealand region: nearshore validation and coastal wave climate’, N.Z. J. Mar. Freshwater Res., 2003, 37, (3), pp. 567–588.
    3. 3)
      • 24. Barker, A., Murphy, J.: ‘Reliability of extreme wave prediction methods’, 2015, pp. 1–8.
    4. 4)
      • 7. Cahill, B.: ‘Wave period ratios and the calculation of wave power’. Second Marine Energy Technology Symp., 2014, pp. 1–10.
    5. 5)
      • 5. Mackay, E.B.L., Bahaj, A.S., Challenor, P.G.: ‘Uncertainty in wave energy resource assessment. Part 1: historic data’, Renew. Energy, 2010, 35, (8), pp. 1792–1808.
    6. 6)
      • 9. Dalton, G.J., Alcorn, R., Lewis, T.: ‘Case study feasibility analysis of the Pelamis wave energy convertor in Ireland, Portugal and North America’, Renew. Energy, 2010, 35, (2), pp. 443–455.
    7. 7)
      • 4. Hughes, M.G., Heap, A.D.: ‘National-scale wave energy resource assessment for Australia’, Renew. Energy, 2010, 35, 8, pp. 1783–1791.
    8. 8)
      • 3. Tolman, H.: ‘The 2002 release of WAVEWATCH III’. Seventh Int. Workshop Wave Hindcasting …, 2002, p. 657575.
    9. 9)
      • 14. Lu, S., Hwang, Y., Khabibrakhmanov, I., et al: ‘Machine learning based multi-physical-model blending for enhancing renewable energy forecast – improvement via situation dependent error correction.*’, 2015, pp. 1–8.
    10. 10)
      • 20. Tucker, M.J., Pitt, E.G.: ‘Waves in ocean engineering’, 2001.
    11. 11)
      • 18. Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J.C., et al: ‘Computational intelligence in wave energy: comprehensive review and case study’, Renew. Sustain. Energy Rev., 2016, 58, pp. 1223–1246.
    12. 12)
      • 8. Reikard, G.: ‘Forecasting ocean wave energy: tests of time-series models’, Ocean Eng., 2009, 36, (5), pp. 348–356.
    13. 13)
      • 19. Fernández, J.C., Salcedo-Sanz, S., Gutiérrez, P.A., et al: ‘Significant wave height and energy flux range forecast with machine learning classifiers’, Eng. Appl. Artif. Intell., 2015, 43, pp. 44–53.
    14. 14)
      • 13. Sharma, N., Sharma, P., Irwin, D., et al: ‘Predicting solar generation from weather forecasts using machine learning’. 2011 IEEE Int. Conf. Smart Grid Communications SmartGridComm 2011, 2011, pp. 528–533.
    15. 15)
      • 15. Sarkar, D., Dias, F.: ‘A machine learning approach to the analysis of wave energy converters’, 2015, pp. 1–9.
    16. 16)
      • 11. Jordan, M.I., Mitchell, T.M.: ‘Machine learning: trends, perspectives, and prospects’, Science (80-.), 2015, 349, (6245), pp. 255–260.
    17. 17)
      • 17. Agrawal, J.D., Deo, M.C.: ‘Wave parameter estimation using neural networks’, Mar. Struct., 2004, 17, (7), pp. 536–550.
    18. 18)
      • 16. Alexandre, E., Cuadra, L., Nieto-Borge, J.C., et al: ‘A hybrid genetic algorithm – extreme learning machine approach for accurate significant wave height reconstruction’, Ocean Model., 2015, 92, pp. 115–123.
    19. 19)
      • 12. Marsland, S.: ‘Machine learning: an algorithmic perspective’ (CRC Press, 2015).
    20. 20)
      • 1. Gallagher, S., Tiron, R., Whelan, E., et al: ‘The nearshore wind and wave energy potential of Ireland: a high resolution assessment of availability and accessibility’, Renew. Energy, 2016, 88, pp. 494–516.
    21. 21)
      • 2. Rascle, N., Ardhuin, F.: ‘A global wave parameter database for geophysical applications. Part 2: model validation with improved source term parameterization’, Ocean Model., 2013, 70, pp. 174–188.
    22. 22)
      • 6. Danish Hydraulic Institute: ‘MIKE21 wave modelling’. 2015, pp. 1–18.
    23. 23)
      • 23. Zheng, Z., Xu, J., Huang, P., et al: ‘Dynamics of anchor last deployment of submersible buoy system’, J. Ocean Univ. China, 2016, 15, (1), pp. 69–77.
    24. 24)
      • 21. Trevor Hastie, J.F., Tibshirani, R.: ‘The elements of statistical learning’, Math. Intell., 2009, 27, (2), pp. 83–85.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0957
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0957
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address