IDEAS home Printed from https://ideas.repec.org/a/wly/complx/v2022y2022i1n8451812.html

Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios

Author

Listed:
  • Abbas Yeganeh-Bakhtiary
  • Hossein EyvazOghli
  • Naser Shabakhty
  • Bahareh Kamranzad
  • Soroush Abolfathi

Abstract

Assessment of climate change impacts on wind characteristics is crucial for the design, operation, and maintenance of coastal and offshore infrastructures. In the present study, the Model Output Statistics (MOS) method was used to downscale a Coupled Model Intercomparison Project Phase 5 (CMIP5) with General Circulation Model (GCM) results for a case study in the North Atlantic Ocean, and a supervised machine learning method (M5’ Decision Tree model) was developed for the first time to establish a statistical relationship between predicator and predicant. To do so, the GCM simulation results and altimeter remote sensing data were employed to examine the capabilities of the M5’DT model in predicting future wind speed and identifying spatiotemporal trends in wind characteristics. For this purpose, three classes of M5′ models were developed to study the annual, seasonal, and monthly variations of wind characteristics. The developed decision tree (DT) models were employed to statistically downscale the Beijing Normal University Earth System Model (BNU‐ESM) global climate model output. The M5′ models are calibrated and successfully validated against the GCM simulation results and altimeter remote sensing data. All the proposed models showed firm outputs in the training section. Predictions from the monthly model with a 70/30 training to test ratio demonstrated the best model performance. The monthly prediction model highlighted the decreasing trend in wind speed relative to the control period in 2030 to 2040 for the case study location and across all three future climate change scenarios tested within this study. This reduction in wind speed reduces wind energy by 13% to 19%.

Suggested Citation

  • Abbas Yeganeh-Bakhtiary & Hossein EyvazOghli & Naser Shabakhty & Bahareh Kamranzad & Soroush Abolfathi, 2022. "Machine Learning as a Downscaling Approach for Prediction of Wind Characteristics under Future Climate Change Scenarios," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:8451812
    DOI: 10.1155/2022/8451812
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/8451812
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/8451812?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
    ---><---

    References listed on IDEAS

    as
    1. Breslow, Paul B. & Sailor, David J., 2002. "Vulnerability of wind power resources to climate change in the continental United States," Renewable Energy, Elsevier, vol. 27(4), pages 585-598.
    2. Reeve, D.E. & Chen, Y. & Pan, S. & Magar, V. & Simmonds, D.J. & Zacharioudaki, A., 2011. "An investigation of the impacts of climate change on wave energy generation: The Wave Hub, Cornwall, UK," Renewable Energy, Elsevier, vol. 36(9), pages 2404-2413.
    3. Segal, Moti & Pan, Zaitao & Arritt, Raymond W & Takle, Eugene S, 2001. "On the potential change in wind power over the US due to increases of atmospheric greenhouse gases," Renewable Energy, Elsevier, vol. 24(2), pages 235-243.
    4. Sailor, D.J & Hu, T & Li, X & Rosen, J.N, 2000. "A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change," Renewable Energy, Elsevier, vol. 19(3), pages 359-378.
    Full references (including those not matched with items on IDEAS)

    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. Lucy Cradden & Gareth Harrison & John Chick, 2012. "Will climate change impact on wind power development in the UK?," Climatic Change, Springer, vol. 115(3), pages 837-852, December.
    2. Schaeffer, Roberto & Szklo, Alexandre Salem & Pereira de Lucena, André Frossard & Moreira Cesar Borba, Bruno Soares & Pupo Nogueira, Larissa Pinheiro & Fleming, Fernanda Pereira & Troccoli, Alberto & , 2012. "Energy sector vulnerability to climate change: A review," Energy, Elsevier, vol. 38(1), pages 1-12.
    3. Jane Ebinger & Walter Vergara, 2011. "Climate Impacts on Energy Systems : Key Issues for Energy Sector Adaptation," World Bank Publications - Books, The World Bank Group, number 2271, April.
    4. Harrison, Gareth P. & Wallace, A. Robin, 2005. "Climate sensitivity of marine energy," Renewable Energy, Elsevier, vol. 30(12), pages 1801-1817.
    5. deCastro, M. & Rusu, L. & Arguilé-Pérez, B. & Ribeiro, A. & Costoya, X. & Carvalho, D. & Gómez-Gesteira, M., 2024. "Different approaches to analyze the impact of future climate change on the exploitation of wave energy," Renewable Energy, Elsevier, vol. 220(C).
    6. Wimhurst, Joshua J. & Greene, J. Scott, 2019. "Oklahoma's future wind energy resources and their relationship with the Central Plains low-level jet," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    7. Sailor, David J. & Smith, Michael & Hart, Melissa, 2008. "Climate change implications for wind power resources in the Northwest United States," Renewable Energy, Elsevier, vol. 33(11), pages 2393-2406.
    8. Yau, Y.H. & Pean, H.L., 2011. "The climate change impact on air conditioner system and reliability in Malaysia—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4939-4949.
    9. Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    10. Pan, Zaitao & Segal, Moti & Arritt, Raymond W & Takle, Eugene S, 2004. "On the potential change in solar radiation over the US due to increases of atmospheric greenhouse gases," Renewable Energy, Elsevier, vol. 29(11), pages 1923-1928.
    11. Majidi, Ajab Gul & Ramos, Victor & Rosa-Santos, Paulo & das Neves, Luciana & Taveira-Pinto, Francisco, 2025. "Power production assessment of wave energy converters in mainland Portugal," Renewable Energy, Elsevier, vol. 243(C).
    12. Li, Jiangxia & Pan, Shunqi & Chen, Yongping & Yao, Yu & Xu, Conghao, 2022. "Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas," Energy, Elsevier, vol. 260(C).
    13. Zheng, Chong-wei, 2021. "Global oceanic wave energy resource dataset—with the Maritime Silk Road as a case study," Renewable Energy, Elsevier, vol. 169(C), pages 843-854.
    14. Akintayo T. Abolude & Wen Zhou & Akintomide Afolayan Akinsanola, 2020. "Evaluation and Projections of Wind Power Resources over China for the Energy Industry Using CMIP5 Models," Energies, MDPI, vol. 13(10), pages 1-16, May.
    15. Penalba, Markel & Giorgi, Giussepe & Ringwood, John V., 2017. "Mathematical modelling of wave energy converters: A review of nonlinear approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1188-1207.
    16. Wang, Bing & Ke, Ruo-Yu & Yuan, Xiao-Chen & Wei, Yi-Ming, 2014. "China׳s regional assessment of renewable energy vulnerability to climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 185-195.
    17. Aissatou Ndiaye & Mounkaila Saley Moussa & Cheikh Dione & Windmanagda Sawadogo & Jan Bliefernicht & Laouali Dungall & Harald Kunstmann, 2022. "Projected Changes in Solar PV and Wind Energy Potential over West Africa: An Analysis of CORDEX-CORE Simulations," Energies, MDPI, vol. 15(24), pages 1-22, December.
    18. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    19. McCubbin, Donald & Sovacool, Benjamin K., 2013. "Quantifying the health and environmental benefits of wind power to natural gas," Energy Policy, Elsevier, vol. 53(C), pages 429-441.
    20. Mahmoodi, Kumars & Fard, Hossein Rezaie & Böling, Jari, 2025. "Long-term climate change effects on power performance of wave energy converters: A case study," Energy, Elsevier, vol. 326(C).

    More about this item

    Statistics

    Access and download statistics

    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:wly:complx:v:2022:y:2022:i:1:n:8451812. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/8503 .

    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.