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A hybrid solution for offshore wind resource assessment from limited onshore measurements

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  • Elshafei, Basem
  • Peña, Alfredo
  • Xu, Dong
  • Ren, Jie
  • Badger, Jake
  • Pimenta, Felipe M.
  • Giddings, Donald
  • Mao, Xuerui

Abstract

In wind resource assessments, which are critical to the pre-construction of wind farms, measurements by LiDARs or masts are a source of high-fidelity data, but are expensive and scarce in space and time, particularly for offshore sites. On the other hand, numerical simulations, using for example the Weather Research and Forecasting (WRF) model, generate temporally and spatially continuous data with relatively low-fidelity. A hybrid approach is proposed here to combine the merit of measurements and simulations for the assessment of offshore wind. Firstly a temporal data fusion using deep Multi Fidelity Gaussian Process Regression (MF-GPR) is performed to combine the intermittent measurement and the continuous simulation data at an onshore location. Then a spatial data fusion using a neural network with Non-linear Autoregression (NAR) and Non-linear Autoregression with external input (NARX) are conducted to project the wind from onshore to offshore. The numerical and measured wind speeds along the west coast of Denmark were used to evaluate the method. We show that the proposed data fusion technique using a gappy onshore measurement results in accurate offshore wind resource assessment within a 2% margin error.

Suggested Citation

  • Elshafei, Basem & Peña, Alfredo & Xu, Dong & Ren, Jie & Badger, Jake & Pimenta, Felipe M. & Giddings, Donald & Mao, Xuerui, 2021. "A hybrid solution for offshore wind resource assessment from limited onshore measurements," Applied Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:appene:v:298:y:2021:i:c:s0306261921006656
    DOI: 10.1016/j.apenergy.2021.117245
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    References listed on IDEAS

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    1. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
    2. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
    3. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
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    Cited by:

    1. Chuan Huang & Changjian Liu & Ming Zhong & Hanbing Sun & Tianhang Gao & Yonglin Zhang, 2024. "Research on Wind Turbine Location and Wind Energy Resource Evaluation Methodology in Port Scenarios," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    2. Elshafei, Basem & Peña, Alfredo & Popov, Atanas & Giddings, Donald & Ren, Jie & Xu, Dong & Mao, Xuerui, 2023. "Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization," Renewable Energy, Elsevier, vol. 202(C), pages 1215-1225.

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