PREDICTION MODEL OF WIND SPEED AND DIRECTION USING DEEP NEURAL NETWORK

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)

JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science)
Vol 3, No 1 (2020): February 2020

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.