PREDICTION MODEL OF WIND SPEED AND DIRECTION USING DEEP NEURAL NETWORK - Anggraini Puspita Sari; Hiroshi Suzuki; Takahiro Kitajima; Takashi Yasuno; Dwi Arman Prasetya (Merdeka Malang University, 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.