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Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models

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Abstract

Renewable energy has gained its significance in the recent years due to the increasing power demand and the requirement in various distribution and utilization sectors. To meet the energy demand, renewable energy resources which include wind and solar have attained significant attractiveness and remarkable expansions are carried out all over the world to enhance the power generation using wind and solar energy. This research paper focuses on predicting the wind speed so that it results in forecasting the possible wind power that can be generated from the wind resources which facilitates to meet the growing energy demand. In this work, a recurrent neural network model called as long short-term memory network model and variants of support vector machine models are used to predict the wind speed for the considered locations where the windmill has been installed. Both these models are tuned for the weight parameters and kernel variational parameters using the proposed hybrid particle swarm optimization algorithm and ant lion optimization algorithm. Experimental simulation results attained prove the validity of the proposed work compared with the methods developed in the early literature.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, RVS College of Engineering and Technology, Coimbatore, 641 402, India

    T. Vinothkumar

  2. Department of Computer Science and Engineering, Kalaignar Karunanidhi Institute of Technology, Coimbatore, 641 402, India

    K. Deeba

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  1. T. Vinothkumar
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  2. K. Deeba
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Correspondence to T. Vinothkumar.

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Vinothkumar, T., Deeba, K. Hybrid wind speed prediction model based on recurrent long short-term memory neural network and support vector machine models. Soft Comput 24, 5345–5355 (2020). https://doi.org/10.1007/s00500-019-04292-w

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