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Short-term wind speed prediction using hybrid machine learning techniques

  • Green Energy for Environmental Sustainability
  • Published:
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Abstract

Wind energy is one of the potential renewable energy sources being exploited around the globe today. Accurate prediction of wind speed is mandatory for precise estimation of wind power at a site. In this study, hybrid machine learning models have been deployed for short-term wind speed prediction. The twin support vector regression (TSVR), primal least squares twin support vector regression (PLSTSVR), iterative Lagrangian twin parametric insensitive support vector regression (ILTPISVR), extreme learning machine (ELM), random vector functional link (RVFL), and large-margin distribution machine-based regression (LDMR) models have been adopted in predicting the short-term wind speed collected from five stations named as Chennai, Coimbatore, Madurai, Salem, and Tirunelveli in Tamil Nadu, India. Further to check the applicability of the models, the performance of the models was compared based on various performance measures like RMSE, MAPE, SMAPE, MASE, SSE/SST, SSR/SST, and R2. The results suggest that LDMR outperforms other models in terms of its prediction accuracy and ELM is computationally faster compared to other models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India

    Deepak Gupta

  2. Department of Civil Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, 642003, India

    Narayanan Natarajan

  3. Department of Civil Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare, Arunachal Pradesh, 791112, India

    Mohanadhas Berlin

Authors
  1. Deepak Gupta
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  2. Narayanan Natarajan
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  3. Mohanadhas Berlin
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Contributions

DG contributed in performing the analysis and preparation of tables; NN contributed in the collection of literature and in writing the introduction; MB contributed in writing and reviewing the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Narayanan Natarajan.

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The authors declare no competing interests.

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Gupta, D., Natarajan, N. & Berlin, M. Short-term wind speed prediction using hybrid machine learning techniques. Environ Sci Pollut Res 29, 50909–50927 (2022). https://doi.org/10.1007/s11356-021-15221-6

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