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Prediction of Wind Speed by Using Machine Learning

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14104))

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Abstract

Due to the depletion of fossil fuel resources and environmental concerns caused by traditional fuel systems in recent years, the share of renewable energy sources in current energy production has been increasing. Among these energy sources, wind and solar energy stand out compared to other sources. Wind energy is a clean, sustainable and low-cost energy source. Wind and solar energies vary considerably according to the stochastic environment of meteorological conditions. Solar and wind energy variability and uncontrollability lead to power quality, generation-consumption balance and reliability problems of solar and wind energy systems. For this reason, it is important to know and predict the wind speed and solar radiation characteristics of the regions where the systems are installed. In this study, meteorological data of Antalya Serik Region were analyzed using statistical methods and wavelet transform. Thus, the potentials of wind and solar energies in the study area and large and small-scale events affecting these potentials were determined. In addition, a short-term estimation study was made for wind intensity and solar radiation using the time series of meteorological data. Besides SARMA, SARMAX and NAR models, Wavelet-NARX, SARMAX-NAR and NAR-SARMAX hybrid models are employed. Hybrid models are successfully produced better results than component forecasts.

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Acknowledgement

This paper is conducted under the Cube4EnvSec project of NATO Science for Peace Program.

Author information

Authors and Affiliations

  1. Faculty of Administrative Sciences, Department of Business Management, Istanbul Aydın University, 34295, Istanbul, Turkey

    Uğur Şener

  2. Department of Computer Engineering, Istanbul Topkapi University, 34087, Istanbul, Turkey

    Buket İşler Kılıç

  3. Faculty of Science and Literature, Süleyman Demirel University, Isparta, Turkey

    Ahmet Tokgözlü

  4. Faculty of Engineering, Department of Computer Engineering, Istanbul Aydın University, 34295, Istanbul, Turkey

    Zafer Aslan

Authors
  1. Uğur Şener
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  2. Buket İşler Kılıç
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  3. Ahmet Tokgözlü
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  4. Zafer Aslan
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Corresponding author

Correspondence to Uğur Şener .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Basilicata, Potenza, Italy

    Francesco Scorza

  6. University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

  7. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

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Şener, U., Kılıç, B.İ., Tokgözlü, A., Aslan, Z. (2023). Prediction of Wind Speed by Using Machine Learning. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14104. Springer, Cham. https://doi.org/10.1007/978-3-031-37105-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-37105-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37104-2

  • Online ISBN: 978-3-031-37105-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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