Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory

Namrye Son, Seunghak Yang and Jeongseung Na
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Namrye Son: Department of Information and Communication Engineering, Honam University, Gwangsan-gu 62399, Korea
Seunghak Yang: Department of Electrical Engineering, Honam University, Gwangsan-gu 62399, Korea
Jeongseung Na: Department of Electrical Engineering, Honam University, Gwangsan-gu 62399, Korea

Energies, 2019, vol. 12, issue 20, 1-17

Abstract: Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

Keywords: wind power prediction; multivariate models; hybrid model; long short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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