Support Vector Machine and Long Short-term Memory using Multivariate Models for Wind Power Forecasting
Eun-Ju Kang1, Nam-Rye Son2

1Eun-Ju Kang, Dept. Of Information and Communication Engineering, Honam University, Gwang-Ju Korea.
2Nam-Rye Son*, Dept. Of Information and Communication Engineering, Honam University, Gwang-Ju Korea.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 2364-2369 | Volume-9 Issue-4, February 2020. | Retrieval Number: D2036029420/2020©BEIESP | DOI: 10.35940/ijitee.D2036.029420
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Abstract: Renewable energy has recently gained considerable attention. In particular, interest in wind energy is rapidly increasing globally. However, the characteristics of instability and volatility in wind energy systems also have a significant on power systems. To address these issues, numerous studies have been carried out to predict wind speed and power. Methods used to forecast wind energy are divided into three categories: physical, data-driven (statistical and artificial intelligence methods), and hybrid methods. In this study, among artificial intelligence methods, we compare short-term wind power using a support vector machine (SVM) and long short-term memory (LSTM). The method using an SVM is a short-term wind power forecast that considers the wind speed and direction on Jeju Island, whereas the method using LSTM does not consider the wind speed and direction. As the experiment results indicate, the SVM method achieves an excellent performance when considering the wind speed and direction. 
Keywords: Wind Power Forecasting, Multivariate Models, Support Vector Machine, Long Short Term Memory
Scope of the Article: Software Engineering Decision Support