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

Abstract

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.

Citation

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. https://doi.org/10.1016/j.renene.2022.12.006

• 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
• DOI: https://doi.org/10.1016/j.renene.2022.12.006
• Keywords: Matrix factorization; Gaussian process regression; Spatiotemporal data fusion; Wind resource assessment
• Public URL: https://nottingham-repository.worktribe.com/output/15939674
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0960148122017918?via%3Dihub