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

Author

Listed:
  • Yunus Yetis
  • Kambiz Tehrani
  • Mo Jamshidi

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.

Suggested Citation

  • Yunus Yetis & Kambiz Tehrani & Mo Jamshidi, 2022. "Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study," Energy and Environment Research, Canadian Center of Science and Education, vol. 12(2), pages 1-11, December.
    • Handle: RePEc:ibn:eerjnl:v:12:y:2022:i:2:p:11
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    References listed on IDEAS

    as
    1. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    2. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
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    Cited by:

    1. Yongtao Sun & Qihui Yu & Xinhao Wang & Shengyu Gao & Guoxin Sun, 2025. "Extraction of Basic Features and Typical Operating Conditions of Wind Power Generation for Sustainable Energy Systems," Sustainability, MDPI, vol. 17(14), pages 1-21, July.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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