Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements
This paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only require...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3659 |
| _version_ | 1827691652095934464 |
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| author | Adrián García-Gutiérrez Diego Domínguez Deibi López Jesús Gonzalo |
| author_facet | Adrián García-Gutiérrez Diego Domínguez Deibi López Jesús Gonzalo |
| author_sort | Adrián García-Gutiérrez |
| collection | DOAJ |
| description | This paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only requires near surface measurements for the prognosis once the neural network is trained. Another advantage is that it can be used to study the wind profile temporal evolution. This work uses data collected by a lidar sensor located at the Universidad de León (Spain). The neural network best configuration was determined using sensibility analyses. The result is a multilayer perceptron with three layers for each altitude: the input layer has six nodes for the last three measurements, the second has 128 nodes and the third consists of two nodes that provide <i>u</i> and <i>v</i>. The proposed method has better performance than traditional methods. The obtained wind profile information obtained is useful for multiple applications, such as preliminary calculations of the wind resource or CFD models. |
| first_indexed | 2024-03-10T11:05:42Z |
| format | Article |
| id | doaj.art-b1d2506ec2ae46df937b48e7dce8e241 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:05:42Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b1d2506ec2ae46df937b48e7dce8e2412023-11-21T21:12:07ZengMDPI AGSensors1424-82202021-05-012111365910.3390/s21113659Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar MeasurementsAdrián García-Gutiérrez0Diego Domínguez1Deibi López2Jesús Gonzalo3Aerospace Engineering Area, Universidad de León, 24071 León, SpainAerospace Engineering Area, Universidad de León, 24071 León, SpainAerospace Engineering Area, Universidad de León, 24071 León, SpainAerospace Engineering Area, Universidad de León, 24071 León, SpainThis paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only requires near surface measurements for the prognosis once the neural network is trained. Another advantage is that it can be used to study the wind profile temporal evolution. This work uses data collected by a lidar sensor located at the Universidad de León (Spain). The neural network best configuration was determined using sensibility analyses. The result is a multilayer perceptron with three layers for each altitude: the input layer has six nodes for the last three measurements, the second has 128 nodes and the third consists of two nodes that provide <i>u</i> and <i>v</i>. The proposed method has better performance than traditional methods. The obtained wind profile information obtained is useful for multiple applications, such as preliminary calculations of the wind resource or CFD models.https://www.mdpi.com/1424-8220/21/11/3659neural networkwind vertical profilelidaratmospheric boundary layer |
| spellingShingle | Adrián García-Gutiérrez Diego Domínguez Deibi López Jesús Gonzalo Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements Sensors neural network wind vertical profile lidar atmospheric boundary layer |
| title | Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements |
| title_full | Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements |
| title_fullStr | Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements |
| title_full_unstemmed | Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements |
| title_short | Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements |
| title_sort | atmospheric boundary layer wind profile estimation using neural networks applied to lidar measurements |
| topic | neural network wind vertical profile lidar atmospheric boundary layer |
| url | https://www.mdpi.com/1424-8220/21/11/3659 |
| work_keys_str_mv | AT adriangarciagutierrez atmosphericboundarylayerwindprofileestimationusingneuralnetworksappliedtolidarmeasurements AT diegodominguez atmosphericboundarylayerwindprofileestimationusingneuralnetworksappliedtolidarmeasurements AT deibilopez atmosphericboundarylayerwindprofileestimationusingneuralnetworksappliedtolidarmeasurements AT jesusgonzalo atmosphericboundarylayerwindprofileestimationusingneuralnetworksappliedtolidarmeasurements |