Vertical wind speed extrapolation: Modelling using a response surface methodology (RSM) based on unconventional designs

Adli, Feriel; Cheggaga, Nawel; Hannane, Farouk; Ouzeri, Leila

doi:10.1177/0309524x211046380

Cited by 1 publication

(2 citation statements)

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“…The random forest and the tree-based genetic programming algorithm start from similar concepts in the way the decisions are generated. Emeksiz's work findings support that we have chosen an ensemble method based on the Random Forest since it is estimated that these algorithms have advantages in solving tasks in nonlinear and complex systems, and can even be combined to generate more robust models [23,25,26].…”

Section: Literature Reviewmentioning

confidence: 78%

“…However, it still outperforms traditional methods. Adli [23] who evaluated a model that includes Response surface methodology (a set of statistical and mathematical techniques useful for the development, improvement, and optimization of processes) to extrapolate the wind speed from 10 m height up to 50 m height. He used WS and temperature data from a meteorological mast and could match a relation between these two variables to improve the predictions done by the ANN model in [20].…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data

Baquero

Torio

Leask³

2022

Energies

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Machine learning (ML) could be used to overcome one of the largest sources of uncertainty in wind resource assessment: to accurately predict the wind speed (WS) at the wind turbine hub height. Therefore, this research defined and evaluated the performance of seven ML supervised algorithms (regressions, decision tree, support vector machines, and an ensemble method) trained with meteorological mast data (temperature, humidity, wind direction, and wind speeds at 50 and 75 m), and mesoscale data below 80 m (from the New European Wind Atlas) to predict the WS at the height of 102 m. The results were compared with the conventional method used in wind energy assessments to vertically extrapolate the WS, the power law. It was proved that the ML models overcome the conventional method in terms of the prediction errors and the coefficient of determination. The main advantage of ML over the power-law was that ML performed the task using either only mesoscale data (described in scenario A), only data from the measurement mast (described in scenario B) or combining these two data sets (described in scenario C). The best ML models were the ensemble method in scenario A with an R2 of 0.63, the linear regression in scenario B with an R2 of 0.97, and the Ridge regressor in scenario C with an R2 of 0.97.

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Section: Literature Reviewmentioning

confidence: 78%

Section: Literature Reviewmentioning

confidence: 99%