Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study

Yunus Yetis, Kambiz Tehrani and Mo Jamshidi

Energy and Environment Research, 2022, vol. 12, issue 2, 11

Abstract: This paper presents a forecasting method to anticipate wind speed accurately. This method is applied to an energy production site using an artificial intelligence method based on machine learning. The accuracy of this method is higher compared to other existing methods in the literature for time series analysis such as artificial neural networks and the complexity of wind speed prediction models without loss of information content. We tested several thousand data between 1985 and 2018 in the city of Basel, a city in northwestern Switzerland. We have used the MATLAB Software for this modeling. The study demonstrates that the use of statistical models based on machine learning is relevant to predict of speed and direction of the wind in power generation systems from meteorological data. The results obtained are presented and discussed.

Date: 2022
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