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The potential of wind energy via an intelligent IoT-oriented assessment

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Abstract

In contemporary times, renewable energy reliability has been an important field of research that is combined with the Internet of Things (IoT) including the opportunities for improving and challenging the work. Wind energy harvesting in IoT (WHIoT)-based framework is investigated considering the associated potential in the historical city of Bam is proposed in this paper. Weibull distribution function, wind power, and energy density were computed for three heights of 40, 60, and 80 m. The results at 80 m show that the maximum monthly wind speed average of 8.48 m/s occurred in July while the minimum value was observed in December with a value of 3.92 m/s. It was demonstrated that during the summer season power density and energy density are at peak values. This is an advantageous observation for Bam city with higher demand for energy during hot summer. The performance of six different wind turbines was assessed in terms of energy production and capacity factor. Finally, an economic assessment was performed to investigate the suitability of Bam city for the installation of small-scale wind turbines.

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Authors and Affiliations

  1. Department of Electrical & Electronic Engineering, Faculty of Engineering, Research Center for AI and IoT, Near East University, Via Mersin 10, Northern Cyprus, Turkey

    Hanifa Teimourian & Kamil Dimililer

  2. Department of Aeronautical Engineering, Faculty of Engineering, University of Kyrenia, Via Mersin 10, Northern Cyprus, Turkey

    Amir Teimourian

  3. Department of Artificial Intelligence Engineering, Faculty of Engineering, Research Center for AI and IoT, Near East University, Via Mersin 10, Northern Cyprus, Turkey

    Fadi Al-Turjman

Authors
  1. Hanifa Teimourian
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  2. Amir Teimourian
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  3. Kamil Dimililer
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  4. Fadi Al-Turjman
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Correspondence to Kamil Dimililer.

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Teimourian, H., Teimourian, A., Dimililer, K. et al. The potential of wind energy via an intelligent IoT-oriented assessment. J Supercomput 78, 5221–5240 (2022). https://doi.org/10.1007/s11227-021-04085-9

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  • DOI: https://doi.org/10.1007/s11227-021-04085-9

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