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Energies 2018, 11(3), 543; doi:10.3390/en11030543

Wind Predictions Upstream Wind Turbines from a LiDAR Database

ETSIAE (School of Aeronautics), Universidad Politécnica de Madrid, E-28040 Madrid, Spain
Author to whom correspondence should be addressed.
Received: 22 January 2018 / Revised: 19 February 2018 / Accepted: 22 February 2018 / Published: 3 March 2018
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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This article presents a new method to predict the wind velocity upstream a horizontal axis wind turbine from a set of light detection and ranging (LiDAR) measurements. The method uses higher order dynamic mode decomposition (HODMD) to construct a reduced order model (ROM) that can be extrapolated in space. LiDAR measurements have been carried out upstream a wind turbine at six different planes perpendicular to the wind turbine axis. This new HODMD-based ROM predicts with high accuracy the wind velocity during a timespan of 24 h in a plane of measurements that is more than 225 m far away from the wind turbine. Moreover, the technique introduced is general and obtained with an almost negligible computational cost. This fact makes it possible to extend its application to both vertical axis wind turbines and real-time operation. View Full-Text
Keywords: Light detection and ranging (LiDAR); wind turbines; prediction; higher order dynamic mode decomposition (HODMD); reduced order model (ROM) Light detection and ranging (LiDAR); wind turbines; prediction; higher order dynamic mode decomposition (HODMD); reduced order model (ROM)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Le Clainche, S.; Lorente, L.S.; Vega, J.M. Wind Predictions Upstream Wind Turbines from a LiDAR Database. Energies 2018, 11, 543.

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