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Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization

Elshafei, Basem; Peña, Alfredo; Popov, Atanas; Giddings, Donald; Ren, Jie; Xu, Dong; Mao, Xuerui


Basem Elshafei

Alfredo Peña

Professor of Engineering Dynamics

Jie Ren

Dong Xu

Xuerui Mao


In the pre-construction of wind farms, wind resource assessment is of paramount importance. Measurements by lidars are a source of high-fidelity data. However, they are expensive and sparse in space and time. Contrarily, Weather Research and Forecasting models generate continuous data with relatively low fidelity. We propose a hybrid approach combining measurements and output from numerical simulations for the assessment of offshore wind. Firstly, the datasets were fed onto a matrix, with columns representing the spatial lidar and WRF points, and the rows representing the time steps. Entries of the matrix reflect the wind speed, empty entries represent unobserved data. Then, matrix factorization using Gaussian process was employed for filling the missing entries with statistically calculated estimates. The model was optimized with stochastic gradient descent to apply GP without approximation methods. To evaluate the method, wind speed data along the coast of Denmark were used. The proposed technique, evaluated using two experiments, resulted in 58% more accurate results than the industrial standard method with trivial increase of computational cost. The RMSE of the proposed method ranges between 0.35 and 0.52 m/s.


Elshafei, B., Peña, A., Popov, A., Giddings, D., Ren, J., Xu, D., & Mao, X. (2023). Offshore wind resource assessment based on scarce spatio-temporal measurements using matrix factorization. Renewable Energy, 202, 1215-1225.

Journal Article Type Article
Acceptance Date Dec 3, 2022
Online Publication Date Dec 8, 2022
Publication Date 2023-01
Deposit Date Feb 10, 2023
Publicly Available Date Feb 13, 2023
Journal Renewable Energy
Print ISSN 0960-1481
Electronic ISSN 1879-0682
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 202
Pages 1215-1225
Keywords Matrix factorization; Gaussian process regression; Spatiotemporal data fusion; Wind resource assessment
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