Abstract
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 u and v. 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.
Highlights
Atmospheric Boundary Layer WindThe atmospheric boundary layer (ABL) is the region of the atmosphere that defines the transition between the upper geostrophic winds and the static air layers in contact with the earth’s surface
All this work has been possible thanks to the ABL wind data collected by a lidar measurement station located at the Universidad de León (Spain) for up to two years
In order to have an operational neural network, we firstly analyze the amount of data required for the training process
Summary
The atmospheric boundary layer (ABL) is the region of the atmosphere that defines the transition between the upper geostrophic winds and the static air layers in contact with the earth’s surface. Most human activities take place in this region, our knowledge and understanding of the ABL is critical for many important applications. Automation [6,7] These activities could be influenced by different atmospheric parameters, wind speed and direction are the most critical ones and have attracted most of the attention. Measuring and/or predicting them is not an easy task. When a precise knowledge of the vertical wind speed profile is needed, in-situ measurement procedures are required. There is a broad range of available technologies
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