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

被引:19
|
作者
Son, Namrye [1 ]
Yang, Seunghak [2 ]
Na, Jeongseung [2 ]
机构
[1] Honam Univ, Dept Informat & Commun Engn, Gwangju 62399, South Korea
[2] Honam Univ, Dept Elect Engn, Gwangju 62399, South Korea
关键词
wind power prediction; multivariate models; hybrid model; long short-term memory; NEURAL-NETWORK; SPEED;
D O I
10.3390/en12203901
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
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页数:17
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