IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v37y2021i1p134-150.html
   My bibliography  Save this article

Probabilistic access forecasting for improved offshore operations

Author

Listed:
  • Gilbert, Ciaran
  • Browell, Jethro
  • McMillan, David

Abstract

Improving access is a priority in the offshore wind sector, driven by the opportunity to increase revenues, reduce costs, and improve safety at operational wind farms. This paper describes a novel method for producing probabilistic forecasts of safety-critical access conditions during crew transfers. Methods of generating density forecasts of significant wave height and peak wave period are developed and evaluated. It is found that boosted semi-parametric models outperform those estimated via maximum likelihood, as well as a non-parametric approach. Scenario forecasts of sea-state variables are generated and used as inputs to a data-driven vessel motion model, based on telemetry recorded during 700 crew transfers. This enables the production of probabilistic access forecasts of vessel motion during crew transfer up to 5 days ahead. The above methodology is implemented on a case study at a wind farm off the east coast of the UK.

Suggested Citation

  • Gilbert, Ciaran & Browell, Jethro & McMillan, David, 2021. "Probabilistic access forecasting for improved offshore operations," International Journal of Forecasting, Elsevier, vol. 37(1), pages 134-150.
    • Handle: RePEc:eee:intfor:v:37:y:2021:i:1:p:134-150
    DOI: 10.1016/j.ijforecast.2020.03.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016920702030056X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2020.03.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    2. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.
    3. Hofner, Benjamin & Mayr, Andreas & Schmid, Matthias, 2016. "gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i01).
    4. Rafael Dawid & David McMillan & Matthew Revie, 2018. "Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty," Energies, MDPI, vol. 11(9), pages 1-17, August.
    5. Silva, Lucas, 2014. "A feature engineering approach to wind power forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 395-401.
    6. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    7. Andreas Mayr & Nora Fenske & Benjamin Hofner & Thomas Kneib & Matthias Schmid, 2012. "Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 403-427, May.
    8. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    9. O'Connor, M. & Lewis, T. & Dalton, G., 2013. "Weather window analysis of Irish west coast wave data with relevance to operations & maintenance of marine renewables," Renewable Energy, Elsevier, vol. 52(C), pages 57-66.
    10. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    11. Irawan, Chandra Ade & Ouelhadj, Djamila & Jones, Dylan & Stålhane, Magnus & Sperstad, Iver Bakken, 2017. "Optimisation of maintenance routing and scheduling for offshore wind farms," European Journal of Operational Research, Elsevier, vol. 256(1), pages 76-89.
    12. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    13. Taylor, James W. & Jeon, Jooyoung, 2018. "Probabilistic forecasting of wave height for offshore wind turbine maintenance," European Journal of Operational Research, Elsevier, vol. 267(3), pages 877-890.
    14. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    15. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ghigo, Alberto & Faraggiana, Emilio & Giorgi, Giuseppe & Mattiazzo, Giuliana & Bracco, Giovanni, 2024. "Floating Vertical Axis Wind Turbines for offshore applications among potentialities and challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Lerche, J. & Lorentzen, S. & Enevoldsen, P. & Neve, H.H., 2022. "The impact of COVID -19 on offshore wind project productivity – A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
    2. Maike Hohberg & Peter Pütz & Thomas Kneib, 2020. "Treatment effects beyond the mean using distributional regression: Methods and guidance," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Boyao Zhang & Tobias Hepp & Sonja Greven & Elisabeth Bergherr, 2022. "Adaptive step-length selection in gradient boosting for Gaussian location and scale models," Computational Statistics, Springer, vol. 37(5), pages 2295-2332, November.
    5. Riccardo De Bin & Vegard Grødem Stikbakke, 2023. "A boosting first-hitting-time model for survival analysis in high-dimensional settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 420-440, April.
    6. Wen, Honglin & Pinson, Pierre & Gu, Jie & Jin, Zhijian, 2024. "Wind energy forecasting with missing values within a fully conditional specification framework," International Journal of Forecasting, Elsevier, vol. 40(1), pages 77-95.
    7. Groll, Andreas & Hambuckers, Julien & Kneib, Thomas & Umlauf, Nikolaus, 2019. "LASSO-type penalization in the framework of generalized additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 140(C), pages 59-73.
    8. Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024. "Online Distributional Regression," Papers 2407.08750, arXiv.org, revised Aug 2024.
    9. Westgate, Bradford S. & Woodard, Dawn B. & Matteson, David S. & Henderson, Shane G., 2016. "Large-network travel time distribution estimation for ambulances," European Journal of Operational Research, Elsevier, vol. 252(1), pages 322-333.
    10. Zhao, Weihua & Lian, Heng & Song, Xinyuan, 2017. "Composite quantile regression for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 15-33.
    11. Jethro Browell, 2018. "Risk Constrained Trading Strategies for Stochastic Generation with a Single-Price Balancing Market," Energies, MDPI, vol. 11(6), pages 1-17, May.
    12. Simon N. Wood, 2020. "Inference and computation with generalized additive models and their extensions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 307-339, June.
    13. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    14. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    15. Henning Schaak & Oliver Musshoff, 2022. "The distribution of the rent–price relationship of agricultural land in Germany," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(3), pages 696-718.
    16. Nicolai Hans & Nadja Klein & Florian Faschingbauer & Michael Schneider & Andreas Mayr, 2023. "Boosting distributional copula regression," Biometrics, The International Biometric Society, vol. 79(3), pages 2298-2310, September.
    17. Stålhane, Magnus & Halvorsen-Weare, Elin E. & Nonås, Lars Magne & Pantuso, Giovanni, 2019. "Optimizing vessel fleet size and mix to support maintenance operations at offshore wind farms," European Journal of Operational Research, Elsevier, vol. 276(2), pages 495-509.
    18. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    19. Amon, Julian & Hornik, Kurt, 2022. "Is it all bafflegab? – Linguistic and meta characteristics of research articles in prestigious economics journals," Journal of Informetrics, Elsevier, vol. 16(2).
    20. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:37:y:2021:i:1:p:134-150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.