PREDICTION MODEL OF WIND SPEED AND DIRECTION USING DEEP NEURAL NETWORK

Authors

  • Anggraini Puspita Sari Tokushima University
  • Hiroshi Suzuki Electrical and Electronic Engineering, Tokushima University
  • Takahiro Kitajima Electrical and Electronic Engineering, Tokushima University
  • Takashi Yasuno Electrical and Electronic Engineering, Tokushima University
  • Dwi Arman Prasetya University of Merdeka Malang

DOI:

https://doi.org/10.26905/jeemecs.v3i1.3946

Keywords:

feed-forward, backpropagation, neural network, wind speed, wind direction.

Abstract

This paper presents the prediction system of wind speed and direction using a feed-forward backpropagation neural network (FFBPNN).  The input of the prediction system is wind speed and direction which are numerical data and provided by Automated Meteorological Data Acquisition System (AMeDAS) in Japan. The performances of the proposed system is evaluated based on mean square error (MSE) between predicted and observed data. In this paper, we substantiate the usefulness of the proposed prediction system improving prediction accuracy compared to four prediction models.

Author Biography

Anggraini Puspita Sari, Tokushima University

I'm student in Electrical and Electronic Engineering, Tokushima University, Japan and lecturer in Electrical Engineering, University of Merdeka Malang, Indonesia

References

N. L. Panwar, S. C. Kaushik, and S. Kothari, “Role of renewable energy sources in environmental protection: A review,†Renew. Sustain. Energy Rev., vol. 15, no. 3, pp. 1513-1524, 2011.

Hitoshi Sori and Takashi Yasuno, “Several Hours Ahead Wind Speed Prediction System Using Correction Recurrent Neural Network,†Proc. International Workshop on Nonlinear Circuits, Communications and Signal Processing (NSCP ’09), 2009, pp. 621-624.

Y. Miyabe, T. Kitajima, and T. Yasuno, “Wind Speed Prediction Model Using Neural Networks Classified By Observed Wind Speed,†Proc RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NSCP 2015), 2015, pp. 230-233.

A. Aguinaga, X. Luo, V. Hidalgo, E. Cando, and F. Llulluna, “A feed-forward backpropagation neural network method for remaining useful life prediction of Francis turbines,†Proc. World Congr. Mech. Chem. Mater. Eng., no. 126, 2017.

A.P. Sari, H. Suzuki, R. Fukuoka, T. Kitajima, and T.Yasuno “Prediction Model of Wind Speed and Direction using Convolutional Neural Network,†Proc. SICE Shikoku Conference, 2019, vol. 2, pp. 23-26.

R. E. Neapolitan and R. E. Neapolitan, Neural Networks and Deep Learning, Yorktown Heights, NY: Springer, 2018, pp. 19-47.

S. Samarasinghe, “Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition,†United States: Auerbach, 2007, pp. 73-109.

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Published

2020-02-27