Abstract
Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.
Highlights
Indonesia is a maritime country in Southeast Asia located between two continents, Asia and Australia, and two oceans, Pacific Ocean and Indian Ocean
An adaptive hybrid model based on Variation Mode Decomposition (VMD), Fruit Fly Optimization Algorithm (FOA), Autoregressive Integrated Moving Average Model (ARIMA) and Deep Belief Network (DBN) is proposed [6]
This study aims to predict short-term wind speed using data over a certain period of observations from the Hang Nadim Badan Meteorologi Klimatologi dan Geofisika (BMKG) station, Batam
Summary
Indonesia is a maritime country in Southeast Asia located between two continents, Asia and Australia, and two oceans, Pacific Ocean and Indian Ocean. Indonesia is an archipelagic country with the largest number of islands in the world. Indonesia’s ocean area is much wider than its land area, make it further strengthens Indonesia's position as the largest archipelagic country in the world. In archipelagic country, studying the possibility of renewable energy, such as wind speed, becomes very important. An adaptive hybrid model based on Variation Mode Decomposition (VMD), Fruit Fly Optimization Algorithm (FOA), Autoregressive Integrated Moving Average Model (ARIMA) and Deep Belief Network (DBN) is proposed [6]. This adaptive hybrid makes the accuracy of the proposed model better than the other models. Zhang [8] performed a study in short-time wind speed prediction using a combined model to obtain better accuracy
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