Application of Novel Strategy Based on Deep Learning for Forecasting Wind Speed

Authors

  • Ananadaraj S. P. Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Manish Srivastava Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Harjinder Singh Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Roshan Jameel Assistant Professor, Department of Artificial Intelligence (AI), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

Wind speed, wind power, renewable energy, prediction, Satin Bowerbird optimization (SBO), Backpropagation Neural Network (BPNN)

Abstract

In several fields, including aviation, environmental management, and the production of energy from renewable sources, exact wind speed forecast is important. The complexity and nonlinearity of the data on wind speed provide difficulties for conventional prediction techniques frequently. In this study, a unique method for improving predictions of wind speed is put forth. It combines the optimization of Satin Bowerbird (SBO) algorithm with the Backpropagation Neural Network (BPNN). The BPNN is ideal for modeling wind speed patterns because it can capture nonlinear interactions. However, the conventional BPNN training procedure is prone to become stuck in local optima, producing predictions that are less than ideal. The SBO technique is used to modify the BPNN characteristics in order to get around this restriction. The performance of the suggested strategy is examined in this study using wind speed information. The suggested SBO-BPNN methodology has the potential to increase the accuracy of wind speed predictions, allowing for better scheduling and making choices in a variety of wind energy-dependent businesses. Future studies should examine how well this model works for similar prediction-related tasks like estimating wind power or predicting wind direction.

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Published

13.12.2023

How to Cite

S. P., A. ., Srivastava, M. ., Singh, H. ., & Jameel, R. . (2023). Application of Novel Strategy Based on Deep Learning for Forecasting Wind Speed. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 639–643. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4247

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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